MCP Tool Reference

Tools are grouped by governed slice and capability category. Each tool lists the scope a key must hold to call it. Use MCP runtime discovery to confirm live availability in a customer environment.

Overview

Slice	Endpoint	Required scope	Tools
Base shared platform	/mcp/base/	base.action.write, base.compute.run, base.read	54
TrafficOps traffic and checkpoints	/mcp/trafficops/	trafficops.read	7
Predictive maintenance	/mcp/pdm/	pdm.read	5
TelcoOps network operations	/mcp/telcoops/	telcoops.read	3
SemiOps semiconductor and cleanroom	/mcp/semiops/	semiops.read	16
Aviation reliability analysis	/mcp/aviation/	aviation.analysis.read, aviation.data.read	11

Base shared platform

Endpoint: /mcp/base/

Tool	Scope	Description
analyze_spare_parts	base.compute.run	Analyze spare parts inventory and usage patterns. Shows top replaced parts, slow-moving inventory, stockout risks, and reorder recommendations. Use for inventory optimization and procurement planning.
analyze_spatial_anomaly	base.compute.run	Analyze spatial heatmap sensor data for anomalies. Detects sensors with values more than N standard deviations from the mean. Use when asked about hot/cold spots, unusual readings, or spatial outliers.
automl_forecast	base.compute.run	AutoML model selection and forecast — picks the best algorithm
calculate_emissions	base.compute.run	Calculate GHG emissions (Scope 1/2/3) for a building or facility. Returns CO₂e breakdown by scope with regional default default emission factors. Use when the user asks about carbon footprint, emissions, or sustainability metrics.
cascade_simulation	base.compute.run	Multi-engine cascade simulation — chain DES, ABM, Monte Carlo
check_data_quality	base.read	Check data quality dashboard for all integrated data sources. Returns quality scores by dimension (completeness, accuracy, consistency, timeliness) and lists top violations. Use when asked about data quality, data health, or data issues.
compare_zones	base.compute.run	Compare spatial statistics between two zones or floors. Provides mean, min, max, std dev comparison with interpretation. Use when asked to compare north vs south, floor 1 vs floor 2, or any two zones.
conformal_predict	base.compute.run	Distribution-free prediction intervals
create_work_order	base.action.write	Create a new work order based on the advisor's recommendation. Only use this when the user explicitly agrees to take action.
detect_anomaly	base.compute.run	Score data points for anomalies (z-score, isolation forest, autoencoder)
detect_drift	base.compute.run	Detect data or concept drift between datasets
estimate_causal_effect	base.compute.run	Estimate treatment effects via causal inference
explain_prediction	base.compute.run	Explain a model prediction using SHAP
extract_maintenance_record	base.read	Extract structured data from maintenance document (PDF/image) using AI. Returns equipment ID, maintenance date, type, technician, findings, replaced parts, and confidence score. Use when the user wants to digitize paper maintenance records.
find_changepoints	base.compute.run	Detect structural breaks in a time series
find_optimal_policy	base.compute.run	Find optimal treatment policy using causal inference
find_path	base.compute.run	Find navigation path between two locations in a building. Supports multi-floor routing, accessibility options, and crowd avoidance.
fit_distribution	base.compute.run	Fit probability distributions to observed data. Useful for analyzing failure times, service durations, or arrival patterns. Returns best-fit distribution with parameters and goodness-of-fit statistics (KS test, AIC). Use when the user wants to understand what statistical distribution best describes their data.
forecast_timeseries	base.compute.run	Run Holt-Winters or Prophet forecast on a time series
generate_report	base.compute.run	Generate a status report or simulation report. Use for equipment overview, alert summary, or simulation analysis reports.
get_action_plan_history	base.read	Get the history of AI action plans including their workflow approval status. Returns past action plans with approval decisions, execution outcomes, and linked ECM documents (incident reports). Use this to answer questions about past incidents, decisions, and their outcomes.
get_compliance_documents	base.read	Get compliance-related documents (certificates, audit reports, evidence packs) filtered by standard. Use when the user asks about ISO compliance, FDA, or regulatory documentation.
get_equipment_documents	base.read	Get all documents associated with an equipment (manuals, SOPs, drawings, maintenance records). Use when the user asks about equipment documentation or wants to find related manuals/SOPs.
get_equipment_status	base.read	Get real-time status of equipment including latest sensor readings, active alerts, and recent work orders. Use this to understand current conditions.
get_expiring_documents	base.read	Get documents approaching retention end date or requiring periodic review. Use for compliance monitoring and proactive document management.
get_optimization_recommendation	base.compute.run	Find the optimal staff configuration for checkpoint operations within a budget constraint. Uses NSGA-II multi-objective optimization to balance throughput vs wait time. Returns Pareto-optimal solutions with cost-benefit analysis and operator-ready actions. Use this to answer questions like 'How should we allocate 10 extra staff?' or 'What's the best configuration for a $5000/hr budget?'.
get_pending_tasks	base.read	Get pending ECM workflow tasks (documents awaiting approval, signature, or review). Use when the user asks about their to-do list or pending approvals.
import_data	base.compute.run	Import and process data from external sources (REST API, CSV) through ETL pipeline for analysis. Connects to a data source, extracts records, and optionally fits distributions to the imported data. Use when the user wants to bring in external data for simulation input modeling.
import_dxf	base.compute.run	Import a DXF floor plan and recognize walls/doors/windows/fences
list_connectors	base.read	List all configured data connectors (REST, CSV, MQTT, OPC-UA, database, etc.) with their current status (active/error/syncing), last sync time, and source info. Use when asked about data sources, integrations, connectors, or data pipelines.
optimize_bayesian	base.compute.run	Bayesian optimization for black-box function tuning
optimize_evolutionary	base.compute.run	Evolutionary multi-objective optimization (NSGA-II)
optimize_layout	base.compute.run	Optimize the spatial layout of a facility (checkpoint positions, capacities, routes) using NSGA-II multi-objective optimization with DES evaluation. Finds Pareto-optimal layouts balancing throughput vs wait time. Use for facility design and space planning.
optimize_milp	base.compute.run	Solve a mixed-integer linear programming (MILP) problem
predict_rul	base.compute.run	Predict remaining useful life (RUL) from sensor readings
predict_surrogate	base.compute.run	Run inference with a trained surrogate model
query_knowledge	base.read	Query the knowledge graph for equipment types, failure modes, repair actions, diagnostic rules, and maintenance schedules. Use this to find expert knowledge.
recommend_model	base.compute.run	AutoML recommendation for best model type
recommend_sensor_placement	base.compute.run	Recommend optimal locations for additional sensors based on spatial coverage gaps and IDW confidence analysis. Use when asked about sensor deployment, coverage gaps, or where to install new sensors.
recommend_training	base.read	Recommend training courses based on equipment type, user role, and identified skill gaps. Returns prioritized course list with duration and priority level. Use for workforce development and certification planning.
run_abm	base.compute.run	Run an agent-based crowd simulation
run_dag_simulation	base.compute.run	Run a DAG-routed DES simulation with advanced routing (shortest-queue, probability, condition). Returns throughput, bottleneck analysis, Sankey flow data, and AI recommendations. Use for complex multi-path checkpoint scenarios.
run_des	base.compute.run	Run a discrete event simulation for process/queue modelling
run_doe	base.compute.run	Run a Design of Experiments to identify which factors most significantly affect a target metric. Returns ANOVA analysis showing factor significance.
run_montecarlo	base.compute.run	Monte Carlo stress test / risk simulation
run_optimization	base.compute.run	Find optimal parameters using multi-objective optimization (NSGA-II). Returns Pareto-optimal solutions trading off competing objectives. Use this when the user wants to find the best configuration.
run_simulation	base.compute.run	Run a discrete event simulation (DES) to test a what-if scenario. Use this to verify predictions, compare configurations, or estimate impact of changes. Available scenes: trafficops (checkpoint flow), heatops (district heating), fms (equipment lifecycle).
run_system_dynamics	base.compute.run	Run a system dynamics (stock-and-flow) simulation
run_what_if_comparison	base.compute.run	Compare the current checkpoint configuration against a modified scenario using DES simulation. Use this to answer questions like 'What if we add 2 staff to biometric-scan?' or 'What happens if the bag scanner fails every 4 hours?'. Returns side-by-side KPI comparison, cost-benefit analysis, and concrete operator actions. Supports staff changes, lane adjustments, and equipment failure injection.
search_checkpoint_sop	base.read	Search checkpoint Standard Operating Procedures (SOPs) stored in the ECM system. Returns relevant SOP documents for a given checkpoint or operation type. Use this when the user asks about procedures, protocols, or standard operations for checkpoint management, immigration control, or customs clearance. Leverages ECM RAG (Retrieval-Augmented Generation) for semantic search.
search_documents	base.read	Search ECM (Enterprise Content Management) for documents by keyword, type, or related entity. Returns document title, version, classification, and direct link. With RAG enhancement enabled, also provides AI-generated summary of relevant documents. Use when the user asks about manuals, SOPs, reports, certificates, or any documentation.
simulate_logistics	base.compute.run	Run AGV/forklift logistics simulation on a facility layout
train_surrogate	base.compute.run	Train a fast surrogate model from data
troubleshoot_connector	base.read	Diagnose a specific data connector by fetching its details and recent sync logs. Analyzes recent errors and suggests concrete fixes (credentials, network, schema mapping, etc.). Use when a connector is failing, data is not syncing, or the user reports import/export issues.

analyze_spare_parts · base.compute.run

Analyze spare parts inventory and usage patterns. Shows top replaced parts, slow-moving inventory, stockout risks, and reorder recommendations. Use for inventory optimization and procurement planning.

Parameters

Parameter	Type	Required	Description
equipment_type	string		Filter by equipment type (e.g. AHU, CHILLER)
part_category	string		Filter by part category
months	integer		Months of history to analyze - default: 12

analyze_spatial_anomaly · base.compute.run

Analyze spatial heatmap sensor data for anomalies. Detects sensors with values more than N standard deviations from the mean. Use when asked about hot/cold spots, unusual readings, or spatial outliers.

Parameters

Parameter	Type	Required	Description
scene_id	string	yes	Scene ID (heatops, iaq-building-env, energy-floor-consumption, space-occupancy)
variable	string	yes	Variable to analyze (supply_temp, co2, electricity)
zone	string		Zone filter: all, north, south, etc. - default: "all"
threshold_sigma	number		Sigma threshold for anomaly detection (default 2.0) - default: 2

automl_forecast · base.compute.run

AutoML model selection and forecast — picks the best algorithm

Parameters

Parameter	Type	Required	Description
values	array	yes
horizon	integer	yes	Forecast steps ahead.
frequency	string		Data frequency: min, h, d, w, m. - default: "h"
metric	string		Evaluation metric. - one of: mape, rmse, mae, smape - default: "mape"
candidates	array		Optional candidate model names.
ensemble	boolean		Create weighted ensemble of top models. - default: true
top_k	integer		Top model count for ensemble. - default: 3

calculate_emissions · base.compute.run

Calculate GHG emissions (Scope 1/2/3) for a building or facility. Returns CO₂e breakdown by scope with regional default default emission factors. Use when the user asks about carbon footprint, emissions, or sustainability metrics.

Parameters

Parameter	Type	Required	Description
scope	integer	yes	Emission scope: 1=direct, 2=electricity, 3=value chain - one of: 1, 2, 3
fuel_type	string		Fuel type for Scope 1 (e.g. natural_gas, diesel, refrigerant_r410a)
electricity_kwh	number		Electricity consumption in kWh for Scope 2
category	string		Category for Scope 3 (e.g. waste_landfill, water_supply, commuting_mrt)
consumption	number		Consumption quantity in the relevant unit
period	string		Reporting period (e.g. '2025-01', '2025-Q1', '2025')

cascade_simulation · base.compute.run

Multi-engine cascade simulation — chain DES, ABM, Monte Carlo

Parameters

Parameter	Type	Required	Description
steps	array	yes
initial_payload	object
continue_on_error	boolean		Continue subsequent engines after a failed step. - default: false

check_data_quality · base.read

Check data quality dashboard for all integrated data sources. Returns quality scores by dimension (completeness, accuracy, consistency, timeliness) and lists top violations. Use when asked about data quality, data health, or data issues.

Parameters

No declared parameters. Discover live details at runtime through tools/list.

compare_zones · base.compute.run

Compare spatial statistics between two zones or floors. Provides mean, min, max, std dev comparison with interpretation. Use when asked to compare north vs south, floor 1 vs floor 2, or any two zones.

Parameters

Parameter	Type	Required	Description
scene_id	string	yes	Scene ID
variable	string	yes	Variable to compare
zone_a	string	yes	First zone (north, 1f, etc.)
zone_b	string	yes	Second zone (south, 2f, etc.)

conformal_predict · base.compute.run

Distribution-free prediction intervals

Parameters

Parameter	Type	Required	Description
train_data	array	yes
test_data	array	yes
target	string	yes	Target column name.
features	array	yes
confidence_levels	array		Confidence levels. - default: [0.9,0.95,0.99]
model_type	string		Base model type. - default: "random_forest"

create_work_order · base.action.write

Create a new work order based on the advisor's recommendation. Only use this when the user explicitly agrees to take action.

Parameters

Parameter	Type	Required	Description
equipment_id	integer	yes	Equipment to create work order for
title	string	yes	Work order title
description	string	yes	Detailed description of work needed
priority	string	yes	one of: LOW, MEDIUM, HIGH, CRITICAL

detect_anomaly · base.compute.run

Score data points for anomalies (z-score, isolation forest, autoencoder)

Parameters

Parameter	Type	Required	Description
readings	array	yes	Sensor readings.
z_threshold	number		Z-score threshold. - default: 3

detect_drift · base.compute.run

Detect data or concept drift between datasets

Parameters

Parameter	Type	Required	Description
values	array	yes	Ordered time-series values.
method	string		Drift method. - one of: adwin, kswin, page_hinkley - default: "adwin"
delta	number		ADWIN delta. - default: 0.002
window_size	integer		KSWIN window size. - default: 100
stat_size	integer		KSWIN stat window size. - default: 30
threshold	number		PageHinkley threshold. - default: 50

estimate_causal_effect · base.compute.run

Estimate treatment effects via causal inference

Parameters

Parameter	Type	Required	Description
data	array	yes
treatment	string	yes	Treatment column.
outcome	string	yes	Outcome column.
features	array
method	string		Estimator method.

explain_prediction · base.compute.run

Explain a model prediction using SHAP

Parameters

Parameter	Type	Required	Description
model_type	string	yes	Model family or registered model type.
features	object	yes
prediction	number		Prediction value to explain.
background_data	array

extract_maintenance_record · base.read

Extract structured data from maintenance document (PDF/image) using AI. Returns equipment ID, maintenance date, type, technician, findings, replaced parts, and confidence score. Use when the user wants to digitize paper maintenance records.

Parameters

Parameter	Type	Required	Description
document_id	integer	yes	ECM document ID to extract

find_changepoints · base.compute.run

Detect structural breaks in a time series

Parameters

Parameter	Type	Required	Description
values	array	yes	Time-series values.
method	string		Changepoint method. - one of: pelt, binary, window, bottomup - default: "pelt"
model	string		Cost model. - default: "rbf"
n_breakpoints	integer		Expected breakpoints for binary/window/bottomup.
min_size	integer		Minimum segment size. - default: 5
penalty	number		Penalty value for PELT.

find_optimal_policy · base.compute.run

Find optimal treatment policy using causal inference

Parameters

Parameter	Type	Required	Description
data	array	yes
treatment	string	yes	Treatment column.
outcome	string	yes	Outcome column.
features	array
policy_constraints	object

find_path · base.compute.run

Find navigation path between two locations in a building. Supports multi-floor routing, accessibility options, and crowd avoidance.

Parameters

Parameter	Type	Required	Description
from_location	string	yes	Starting location name or node ID
to_location	string	yes	Destination location name or node ID
accessible	boolean		Wheelchair-accessible route only - default: false
avoid_crowds	boolean		Avoid congested areas - default: false

fit_distribution · base.compute.run

Fit probability distributions to observed data. Useful for analyzing failure times, service durations, or arrival patterns. Returns best-fit distribution with parameters and goodness-of-fit statistics (KS test, AIC). Use when the user wants to understand what statistical distribution best describes their data.

Parameters

Parameter	Type	Required	Description
data_source	string	yes	Source of data to fit. 'custom' expects raw data array. - one of: sensor_readings, failure_times, service_times, custom
equipment_id	integer		Equipment ID for sensor/failure data (required for sensor_readings, failure_times)
sensor_type	string		Sensor type filter (e.g. 'temperature', 'vibration') for sensor_readings
custom_data	array		Raw data points for custom fitting (min 20 points)

forecast_timeseries · base.compute.run

Run Holt-Winters or Prophet forecast on a time series

Parameters

Parameter	Type	Required	Description
model_name	string		Trained Prophet model name. - default: "default"
horizon	integer		Number of future periods to forecast. - default: 30
frequency	string		Forecast frequency: D, H, W. - default: "D"

generate_report · base.compute.run

Generate a status report or simulation report. Use for equipment overview, alert summary, or simulation analysis reports.

Parameters

Parameter	Type	Required	Description
report_type	string	yes	Type of report: 'simulation' runs a DES and reports KPIs, 'equipment_status' summarizes current equipment/alerts/work orders - one of: simulation, equipment_status
module	string		Module for simulation reports - one of: trafficops, heatops, fms
format	string		Output format (default: pdf) - one of: pdf, excel

get_action_plan_history · base.read

Get the history of AI action plans including their workflow approval status. Returns past action plans with approval decisions, execution outcomes, and linked ECM documents (incident reports). Use this to answer questions about past incidents, decisions, and their outcomes.

Parameters

Parameter	Type	Required	Description
checkpoint_id	string		Optional: filter by checkpoint ID
urgency	string		Filter by urgency level. Default ALL - one of: CRITICAL, WARNING, ALL - default: "ALL"
limit	integer		Maximum number of results to return. Default 10 - default: 10

get_compliance_documents · base.read

Get compliance-related documents (certificates, audit reports, evidence packs) filtered by standard. Use when the user asks about ISO compliance, FDA, or regulatory documentation.

Parameters

Parameter	Type	Required	Description
standard	string		Compliance standard (e.g. ISO_14644, SEMI_S2, GM, FDA_21_CFR)
status	string		Filter by document status - one of: APPROVED, EXPIRED, IN_REVIEW, RECORD

get_equipment_documents · base.read

Get all documents associated with an equipment (manuals, SOPs, drawings, maintenance records). Use when the user asks about equipment documentation or wants to find related manuals/SOPs.

Parameters

Parameter	Type	Required	Description
equipment_id	integer	yes	Equipment ID
doc_type	string		Filter by document type - one of: MANUAL, SOP, DRAWING, REPORT, CERTIFICATE, PHOTO

get_equipment_status · base.read

Get real-time status of equipment including latest sensor readings, active alerts, and recent work orders. Use this to understand current conditions.

Parameters

Parameter	Type	Required	Description
equipment_id	integer		Equipment ID to query (omit for all equipment)

get_expiring_documents · base.read

Get documents approaching retention end date or requiring periodic review. Use for compliance monitoring and proactive document management.

Parameters

Parameter	Type	Required	Description
days_ahead	integer		Days ahead to check (default: 30) - default: 30

get_optimization_recommendation · base.compute.run

Find the optimal staff configuration for checkpoint operations within a budget constraint. Uses NSGA-II multi-objective optimization to balance throughput vs wait time. Returns Pareto-optimal solutions with cost-benefit analysis and operator-ready actions. Use this to answer questions like 'How should we allocate 10 extra staff?' or 'What's the best configuration for a $5000/hr budget?'.

Parameters

Parameter	Type	Required	Description
budget	number		Total hourly budget for staff (cost units). Default 5000 - default: 5000
cost_per_staff	number		Cost per additional staff member per hour. Default 100 - default: 100
target_kpi	string		Primary KPI to optimize - one of: avg_wait, throughput, p95_wait - default: "avg_wait"
audience	string		Target audience for the insight report - one of: manager, operator, both - default: "both"

get_pending_tasks · base.read

Get pending ECM workflow tasks (documents awaiting approval, signature, or review). Use when the user asks about their to-do list or pending approvals.

Parameters

Parameter	Type	Required	Description
user_id	integer		User ID (omit for current user)

import_data · base.compute.run

Import and process data from external sources (REST API, CSV) through ETL pipeline for analysis. Connects to a data source, extracts records, and optionally fits distributions to the imported data. Use when the user wants to bring in external data for simulation input modeling.

Parameters

Parameter	Type	Required	Description
connector_type	string	yes	Type of data connector: 'rest' for REST API, 'csv' for CSV file - one of: rest, csv
endpoint	string	yes	URL for REST API or file path for CSV
pipeline_id	string		Optional ETL pipeline: 'arrival-fitting' or 'service-time' - one of: arrival-fitting, service-time
field_mapping	object		Optional source→target field mapping (e.g. {'timestamp': 'arrival_time'})

import_dxf · base.compute.run

Import a DXF floor plan and recognize walls/doors/windows/fences

Parameters

Parameter	Type	Required	Description
file_path	string		Server-side DXF path.
content	string		DXF content when file_path is not used.
layers	array
recognize	boolean		Recognize walls/doors/windows/fences. - default: true

list_connectors · base.read

List all configured data connectors (REST, CSV, MQTT, OPC-UA, database, etc.) with their current status (active/error/syncing), last sync time, and source info. Use when asked about data sources, integrations, connectors, or data pipelines.

Parameters

No declared parameters. Discover live details at runtime through tools/list.

optimize_bayesian · base.compute.run

Bayesian optimization for black-box function tuning

Parameters

Parameter	Type	Required	Description
parameters	array	yes
objective_name	string		Objective label. - default: "objective"
direction	string		Optimization direction. - one of: minimize, maximize - default: "minimize"
n_trials	integer		Trial count. - default: 50
evaluations	array
sampler	string		Sampler. - one of: tpe, cmaes, random - default: "tpe"

optimize_evolutionary · base.compute.run

Evolutionary multi-objective optimization (NSGA-II)

Parameters

Parameter	Type	Required	Description
variables	array	yes
objectives	array	yes
constraints	array
population_size	integer		Population size. - default: 100
generations	integer		Generation count. - default: 50
seed	integer		Optional random seed.

optimize_layout · base.compute.run

Optimize the spatial layout of a facility (checkpoint positions, capacities, routes) using NSGA-II multi-objective optimization with DES evaluation. Finds Pareto-optimal layouts balancing throughput vs wait time. Use for facility design and space planning.

Parameters

Parameter	Type	Required	Description
template_id	string	yes	Layout template to optimize - one of: immigration-hall-small, security-screening, departure-lounge
objectives	array		Objectives to optimize (default: throughput, avg_wait_time)
pop_size	integer		NSGA-II population size (default: 20)
n_gen	integer		Number of generations (default: 30)

optimize_milp · base.compute.run

Solve a mixed-integer linear programming (MILP) problem

Parameters

Parameter	Type	Required	Description
variables	array	yes
objective	object	yes
constraints	array

predict_rul · base.compute.run

Predict remaining useful life (RUL) from sensor readings

Parameters

Parameter	Type	Required	Description
equipment_id	string	yes	Equipment identifier.
health_data	array	yes	Recent health indicator values.
failure_history	array		Optional historical failure times.

predict_surrogate · base.compute.run

Run inference with a trained surrogate model

Parameters

Parameter	Type	Required	Description
model_name	string	yes	Model name.
inputs	array	yes

query_knowledge · base.read

Query the knowledge graph for equipment types, failure modes, repair actions, diagnostic rules, and maintenance schedules. Use this to find expert knowledge.

Parameters

Parameter	Type	Required	Description
query_type	string	yes	Type of knowledge to query - one of: equipment_info, failure_modes, repair_actions, diagnostic_rules
equipment_type	string		Equipment type (e.g. COMPRESSOR, AHU, PUMP, CHILLER)
keyword	string		Search keyword for free-text knowledge search

recommend_model · base.compute.run

AutoML recommendation for best model type

Parameters

Parameter	Type	Required	Description
values	array	yes	Series values.
task	string		Task type. - one of: forecast, anomaly - default: "forecast"

recommend_sensor_placement · base.compute.run

Recommend optimal locations for additional sensors based on spatial coverage gaps and IDW confidence analysis. Use when asked about sensor deployment, coverage gaps, or where to install new sensors.

Parameters

Parameter	Type	Required	Description
scene_id	string	yes	Scene ID
variable	string	yes	Variable to analyze for coverage
zone	string		Zone to analyze. Use all for full area. - default: "all"
max_recommendations	integer		Maximum number of placement recommendations (default 5) - default: 5

recommend_training · base.read

Recommend training courses based on equipment type, user role, and identified skill gaps. Returns prioritized course list with duration and priority level. Use for workforce development and certification planning.

Parameters

Parameter	Type	Required	Description
equipment_type	string		Equipment type (e.g. AHU, CHILLER, COMPRESSOR)
user_role	string		User role for role-specific training - one of: operator, technician, engineer, manager - default: "operator"
skill_gap	string		Identified skill gap to address

run_abm · base.compute.run

Run an agent-based crowd simulation

Parameters

Parameter	Type	Required	Description
width	integer	yes	Grid width.
height	integer	yes	Grid height.
num_agents	integer	yes	Agent count.
exits	array	yes
obstacles	array
steps	integer		Simulation steps. - default: 100

run_dag_simulation · base.compute.run

Run a DAG-routed DES simulation with advanced routing (shortest-queue, probability, condition). Returns throughput, bottleneck analysis, Sankey flow data, and AI recommendations. Use for complex multi-path checkpoint scenarios.

Parameters

Parameter	Type	Required	Description
scene_id	string	yes	DAG scene to simulate - one of: cp-immigration-dag, cp-security-dag, cp-multi-terminal
simulation_time	number		Simulation duration in minutes (default: 120)
staff_count	integer		Number of staff/lanes (affects capacity)

run_des · base.compute.run

Run a discrete event simulation for process/queue modelling

Parameters

Parameter	Type	Required	Description
sceneType	string	yes	Registered DES scene type.
sceneId	string	yes	Scene configuration id.
simulationTime	number		Simulation time in minutes. - default: 480
seed	integer		Optional random seed.
replications	integer		Replication count. - default: 1
moduleConfig	object
playback	boolean		Emit replay events. - default: false
parallel	boolean		Run replications in parallel.
maxWorkers	integer		Max parallel workers. - default: 4
shiftSchedule	array
failureConfig	object

run_doe · base.compute.run

Run a Design of Experiments to identify which factors most significantly affect a target metric. Returns ANOVA analysis showing factor significance.

Parameters

Parameter	Type	Required	Description
scene_type	string	yes	one of: trafficops, heatops, fms
scene_id	string	yes
factors	array	yes	Factors to vary in the experiment
response_metric	string	yes	KPI to analyze (e.g. throughput, availability, total_heat_delivered_kj)

run_montecarlo · base.compute.run

Monte Carlo stress test / risk simulation

Parameters

Parameter	Type	Required	Description
model_name	string	yes	Human-readable model name.
parameters	object	yes	Parameter name -> distribution spec {distribution,args}.
output_expression	string	yes	Safe Python expression referencing sampled parameters.
n_simulations	integer		Number of iterations. - default: 10000
confidence_level	number		Confidence level. - default: 0.95

run_optimization · base.compute.run

Find optimal parameters using multi-objective optimization (NSGA-II). Returns Pareto-optimal solutions trading off competing objectives. Use this when the user wants to find the best configuration.

Parameters

Parameter	Type	Required	Description
module_type	string	yes	one of: trafficops, heatops, fms
population_size	integer		default: 20
generations	integer		default: 10

run_simulation · base.compute.run

Run a discrete event simulation (DES) to test a what-if scenario. Use this to verify predictions, compare configurations, or estimate impact of changes. Available scenes: trafficops (checkpoint flow), heatops (district heating), fms (equipment lifecycle).

Parameters

Parameter	Type	Required	Description
scene_type	string	yes	Type of simulation to run - one of: trafficops, heatops, fms
scene_id	string	yes	Scene configuration ID (e.g. 'rts-main-hall', 'small-network', 'hvac-fleet')
simulation_time	number		Simulation time in minutes - default: 480
config_overrides	object		Override scene parameters (e.g. num_counters, supply_temp)

run_system_dynamics · base.compute.run

Run a system dynamics (stock-and-flow) simulation

Parameters

Parameter	Type	Required	Description
stocks	object	yes	Stock name -> initial value.
flows	array
auxiliaries	array
parameters	object		Model parameters.
dt	number		Integration timestep. - default: 0.25
duration	number	yes	Total simulation time.

run_what_if_comparison · base.compute.run

Compare the current checkpoint configuration against a modified scenario using DES simulation. Use this to answer questions like 'What if we add 2 staff to biometric-scan?' or 'What happens if the bag scanner fails every 4 hours?'. Returns side-by-side KPI comparison, cost-benefit analysis, and concrete operator actions. Supports staff changes, lane adjustments, and equipment failure injection.

Parameters

Parameter	Type	Required	Description
changes	object		Per-checkpoint overrides: {checkpoint_id: {staff_count, mean_service_time, counters}}. Example: {'biometric-scan': {'staff_count': 4}, 'bag-scan': {'staff_count': 3}}
failure_injection	object		Per-checkpoint failure configs: {checkpoint_id: {mtbf, mttr}}. Example: {'biometric-scan': {'mtbf': 240, 'mttr': 15}} — scanner fails every 4h, 15min repair
label	string		Human-readable label for the modified scenario - default: "Modified Scenario"
audience	string		Target audience for the insight report - one of: manager, operator, both - default: "both"
replications	integer		Number of simulation replications (higher = more accurate, slower) - default: 5

search_checkpoint_sop · base.read

Search checkpoint Standard Operating Procedures (SOPs) stored in the ECM system. Returns relevant SOP documents for a given checkpoint or operation type. Use this when the user asks about procedures, protocols, or standard operations for checkpoint management, immigration control, or customs clearance. Leverages ECM RAG (Retrieval-Augmented Generation) for semantic search.

Parameters

Parameter	Type	Required	Description
query	string	yes	Search query for SOP content (e.g. 'peak hour lane opening procedure', 'biometric scanner fallback protocol', 'VIP passenger handling')
checkpoint_id	string		Optional: specific checkpoint ID to scope the search
doc_type	string		Type of document to search. Default ALL - one of: SOP, INCIDENT_REPORT, CAPACITY_PLANNING, ALL - default: "ALL"

search_documents · base.read

Search ECM (Enterprise Content Management) for documents by keyword, type, or related entity. Returns document title, version, classification, and direct link. With RAG enhancement enabled, also provides AI-generated summary of relevant documents. Use when the user asks about manuals, SOPs, reports, certificates, or any documentation.

Parameters

Parameter	Type	Required	Description
query	string		Search keyword (title, description, content)
doc_type	string		Filter by document type - one of: MANUAL, SOP, REPORT, DRAWING, CERTIFICATE, PHOTO, CONTRACT, TEMPLATE
entity_type	string		Filter by related entity type - one of: EQUIPMENT, ALERT, WORK_ORDER, CLEANROOM, SMT_LINE
entity_id	integer		Entity ID to filter by
use_rag	boolean		Enable RAG-based AI summary - default: true

simulate_logistics · base.compute.run

Run AGV/forklift logistics simulation on a facility layout

Parameters

Parameter	Type	Required	Description
layout	object	yes
agvs	array
tasks	array
simulationTime	number		Simulation time. - default: 480
seed	integer		Optional random seed.

train_surrogate · base.compute.run

Train a fast surrogate model from data

Parameters

Parameter	Type	Required	Description
model_name	string	yes	Model name.
inputs	array	yes
outputs	array	yes
model_type	string		Surrogate model type. - default: "random_forest"
test_size	number		Validation split. - default: 0.2

troubleshoot_connector · base.read

Diagnose a specific data connector by fetching its details and recent sync logs. Analyzes recent errors and suggests concrete fixes (credentials, network, schema mapping, etc.). Use when a connector is failing, data is not syncing, or the user reports import/export issues.

Parameters

Parameter	Type	Required	Description
connector_name	string	yes	Name or partial name of the connector to troubleshoot

TrafficOps traffic and checkpoints

Endpoint: /mcp/trafficops/

Tool	Scope	Description
check_officer_roster	trafficops.read	Check current shift officer roster and manpower availability at the checkpoint. Returns all deployed officers with their assignments, available spares, and next shift change time. Use this when asked about staffing, manpower, or whether additional officers can be deployed.
evaluate_lane_reconfig	trafficops.read	Run a DES (Discrete Event Simulation) comparing the current lane configuration against a proposed reconfiguration (e.g., closing a car lane to open an additional motorcycle lane). Uses real simulation engine to compute wait times, throughput, and SLA compliance. You can provide user-reported data like arrival rates and queue lengths for accurate simulation. Use this when recommending lane changes to quantify the trade-off before the officer decides.
get_checkpoint_lane_status	trafficops.read	Get real-time lane status for a vehicle checkpoint including per-lane utilization, queue lengths, wait times, and assigned officers. Covers both motorcycle and car lanes. You can provide user-reported data (queue lengths, arrival rates, lane counts) to override defaults. Use this when asked about current checkpoint conditions, congestion, or lane capacity.
get_proactive_alerts	trafficops.read	Get proactive congestion alerts based on forecast vs SLA comparison. Returns predicted SLA breaches with severity, evidence, and improvement suggestions (Budget/Speed/Balanced). Use this when asked about potential upcoming issues or congestion risks.
get_surge_detection	trafficops.read	Get current traffic surge/anomaly detection status for a checkpoint. Detects unusual spikes in arrival rates by vehicle type (motorcycle, car, bus). Returns surge magnitude, estimated duration, probable cause, and initial recommendation. You can provide user-reported arrival rates to override defaults. Use this when asked about current traffic anomalies or unexpected congestion.
get_traffic_forecast	trafficops.read	Get an 8-hour traffic flow forecast for a specific checkpoint. Returns predicted throughput (pax/h) at 15-minute intervals with confidence bands. Use this to check if congestion is expected and plan ahead.
get_traffic_patterns	trafficops.read	Get detected recurring patterns for a traffic checkpoint using DOE statistical analysis. Returns day-of-week effects, hour-of-day peaks, and bottleneck patterns with p-values and confidence levels. Use this to understand structural traffic behavior.

check_officer_roster · trafficops.read

Check current shift officer roster and manpower availability at the checkpoint. Returns all deployed officers with their assignments, available spares, and next shift change time. Use this when asked about staffing, manpower, or whether additional officers can be deployed.

Parameters

Parameter	Type	Required	Description
shift	string		Shift to check: 'current', 'morning', 'afternoon', 'night' - default: "current"

evaluate_lane_reconfig · trafficops.read

Run a DES (Discrete Event Simulation) comparing the current lane configuration against a proposed reconfiguration (e.g., closing a car lane to open an additional motorcycle lane). Uses real simulation engine to compute wait times, throughput, and SLA compliance. You can provide user-reported data like arrival rates and queue lengths for accurate simulation. Use this when recommending lane changes to quantify the trade-off before the officer decides.

Parameters

Parameter	Type	Required	Description
close_lanes	array	yes	Lane IDs to close (e.g. ['CAR-4'])
open_lanes	array	yes	New lane configs to open (e.g. [{'id': 'MC-6', 'type': 'motorcycle', 'from_lane': 'CAR-4'}])
motorcycle_arrival_rate_hr	number		Motorcycle arrival rate in vehicles/hour (default: 420 for surge scenario)
car_arrival_rate_hr	number		Car arrival rate in vehicles/hour (default: 180)
motorcycle_lanes	integer		Current number of motorcycle lanes in baseline (default: 5)
car_lanes	integer		Current number of car lanes in baseline (default: 4)
motorcycle_queue_length	integer		Current motorcycle queue length (total vehicles waiting, default: 0)
car_queue_length	integer		Current car queue length (total vehicles waiting, default: 0)
simulation_time_min	number		Simulation duration in minutes (default: 60)

get_checkpoint_lane_status · trafficops.read

Get real-time lane status for a vehicle checkpoint including per-lane utilization, queue lengths, wait times, and assigned officers. Covers both motorcycle and car lanes. You can provide user-reported data (queue lengths, arrival rates, lane counts) to override defaults. Use this when asked about current checkpoint conditions, congestion, or lane capacity.

Parameters

Parameter	Type	Required	Description
scene_id	string		Scene ID (e.g. border-lbc-arrival-car, border-lbc-departure-car) - default: "border-lbc-arrival-car"
motorcycle_lanes	integer		Override number of motorcycle lanes (default: 5)
car_lanes	integer		Override number of car lanes (default: 4)
motorcycle_queue_total	integer		User-reported total motorcycle queue length across all lanes
car_queue_total	integer		User-reported total car queue length across all lanes
motorcycle_arrival_rate_hr	number		User-reported motorcycle arrival rate (vehicles/hour)
car_arrival_rate_hr	number		User-reported car arrival rate (vehicles/hour)

get_proactive_alerts · trafficops.read

Get proactive congestion alerts based on forecast vs SLA comparison. Returns predicted SLA breaches with severity, evidence, and improvement suggestions (Budget/Speed/Balanced). Use this when asked about potential upcoming issues or congestion risks.

Parameters

Parameter	Type	Required	Description
sla_minutes	number		SLA threshold in minutes for wait time - default: 12

get_surge_detection · trafficops.read

Get current traffic surge/anomaly detection status for a checkpoint. Detects unusual spikes in arrival rates by vehicle type (motorcycle, car, bus). Returns surge magnitude, estimated duration, probable cause, and initial recommendation. You can provide user-reported arrival rates to override defaults. Use this when asked about current traffic anomalies or unexpected congestion.

Parameters

Parameter	Type	Required	Description
checkpoint	string		Checkpoint area (e.g. vehicle-arrival, pedestrian-arrival) - default: "vehicle-arrival"
motorcycle_arrival_rate_hr	number		User-reported motorcycle arrival rate (vehicles/hour)
car_arrival_rate_hr	number		User-reported car arrival rate (vehicles/hour)
motorcycle_lanes	integer		Number of motorcycle lanes (for affected lanes list)

get_traffic_forecast · trafficops.read

Get an 8-hour traffic flow forecast for a specific checkpoint. Returns predicted throughput (pax/h) at 15-minute intervals with confidence bands. Use this to check if congestion is expected and plan ahead.

Parameters

Parameter	Type	Required	Description
checkpoint	string		Checkpoint ID (e.g. document-check, biometric-scan, security-screen) - default: "document-check"
hours	integer		Hours ahead to forecast (1-24) - default: 8

get_traffic_patterns · trafficops.read

Get detected recurring patterns for a traffic checkpoint using DOE statistical analysis. Returns day-of-week effects, hour-of-day peaks, and bottleneck patterns with p-values and confidence levels. Use this to understand structural traffic behavior.

Parameters

Parameter	Type	Required	Description
checkpoint	string		Checkpoint ID - default: "document-check"
days	integer		Days of historical data to analyze - default: 90

Predictive maintenance

Endpoint: /mcp/pdm/

Tool	Scope	Description
get_equipment_health	pdm.read	Get health status of a predictive maintenance equipment. Returns health score (0-100), grade (A/B/C/D/F), anomaly level, crest factor, vibration RMS.
get_filter_circular_recovery	pdm.read	Get live circular recovery and aftermarket outlook for predictive maintenance filter components in the current tenant or one equipment. Returns recovery candidates, risk band, remaining life, recommended actions, and aftermarket or disposal guidance. Use for circular recovery, reuse, remanufacture, disposal, aftermarket, or sustainability questions about filter components.
get_filter_component_intelligence	pdm.read	Get live predictive maintenance filter component intelligence for the current tenant or one equipment. Returns components that need attention now, including risk band, predicted remaining life, recommended action, benchmark context, and aftermarket narrative. Use for questions about filter components, component intelligence, customer fleet filters, what needs attention right now, or component-level maintenance priorities.
get_pdm_summary	pdm.read	Get predictive maintenance fleet health summary: equipment count, grade distribution (A/B/C/D/F), critical count, active alerts, 7-day health trend.
list_pdm_anomalies	pdm.read	List predictive maintenance anomalies: bearing wear, overheating, vibration excess. Returns type, severity, equipment, AI recommendation.

get_equipment_health · pdm.read

Get health status of a predictive maintenance equipment. Returns health score (0-100), grade (A/B/C/D/F), anomaly level, crest factor, vibration RMS.

Parameters

Parameter	Type	Required	Description
equipment_name	string	yes	Equipment name or code (e.g. VB-VP-001, vacuum pump)
equipment_id	string		Equipment UUID (optional)

get_filter_circular_recovery · pdm.read

Get live circular recovery and aftermarket outlook for predictive maintenance filter components in the current tenant or one equipment. Returns recovery candidates, risk band, remaining life, recommended actions, and aftermarket or disposal guidance. Use for circular recovery, reuse, remanufacture, disposal, aftermarket, or sustainability questions about filter components.

Parameters

Parameter	Type	Required	Description
equipment_id	string		Equipment ID (optional)
equipment_code	string		Equipment code or name, e.g. MH-MP-001 (optional)
top_n	integer		How many components to highlight (default: 5)

get_filter_component_intelligence · pdm.read

Get live predictive maintenance filter component intelligence for the current tenant or one equipment. Returns components that need attention now, including risk band, predicted remaining life, recommended action, benchmark context, and aftermarket narrative. Use for questions about filter components, component intelligence, customer fleet filters, what needs attention right now, or component-level maintenance priorities.

Parameters

Parameter	Type	Required	Description
equipment_id	string		Equipment ID (optional)
equipment_code	string		Equipment code or name, e.g. MH-EX-003 (optional)
top_n	integer		How many components to highlight (default: 5)

get_pdm_summary · pdm.read

Get predictive maintenance fleet health summary: equipment count, grade distribution (A/B/C/D/F), critical count, active alerts, 7-day health trend.

Parameters

Parameter	Type	Required	Description
tenant_filter	string

list_pdm_anomalies · pdm.read

List predictive maintenance anomalies: bearing wear, overheating, vibration excess. Returns type, severity, equipment, AI recommendation.

Parameters

Parameter	Type	Required	Description
severity	string		one of: ALL, CRITICAL, HIGH, MEDIUM, LOW
status	string		one of: OPEN, RESOLVED, ALL
limit	integer

TelcoOps network operations

Endpoint: /mcp/telcoops/

Tool	Scope	Description
analyze_network_health	telcoops.read	Analyze telecom network health by fetching overview, link utilization, and open incidents. Returns a graded narrative (A-F) with node/link counts, incident summary, top-risk link, financial impact estimate, and prioritized recommendations. Use when asked about network status, NOC overview, telecom health, or infrastructure risk.
explain_incident	telcoops.read	Explain a specific telecom network incident in natural language. Returns severity, detection time, root cause, customer/revenue impact, and step-by-step remediation actions (immediate, short-term, long-term). Use when asked about a particular incident, alert, or fault.
predict_capacity	telcoops.read	Predict link capacity breaches based on current utilization and growth trends. Identifies links above 70% utilization, estimates days to breach threshold, and returns CapEx requirements and SLA penalty exposure if upgrades are deferred. Use when asked about capacity planning, bandwidth forecasting, or upgrade priorities.

analyze_network_health · telcoops.read

Analyze telecom network health by fetching overview, link utilization, and open incidents. Returns a graded narrative (A-F) with node/link counts, incident summary, top-risk link, financial impact estimate, and prioritized recommendations. Use when asked about network status, NOC overview, telecom health, or infrastructure risk.

Parameters

No declared parameters. Discover live details at runtime through tools/list.

explain_incident · telcoops.read

Explain a specific telecom network incident in natural language. Returns severity, detection time, root cause, customer/revenue impact, and step-by-step remediation actions (immediate, short-term, long-term). Use when asked about a particular incident, alert, or fault.

Parameters

Parameter	Type	Required	Description
incident_id	string	yes	Incident ID to explain

predict_capacity · telcoops.read

Predict link capacity breaches based on current utilization and growth trends. Identifies links above 70% utilization, estimates days to breach threshold, and returns CapEx requirements and SLA penalty exposure if upgrades are deferred. Use when asked about capacity planning, bandwidth forecasting, or upgrade priorities.

Parameters

No declared parameters. Discover live details at runtime through tools/list.

SemiOps semiconductor and cleanroom

Endpoint: /mcp/semiops/

Tool	Scope	Description
analyze_env_correlation	semiops.read	Analyze correlation between environmental parameters (temperature, humidity, pressure, particles) in a cleanroom. Helps identify which parameters influence each other. Especially useful for diagnosing temperature/humidity impact on solder paste performance, PCB lamination quality, and photolithography exposure accuracy.
classify_smt_defects	semiops.read	Classify SMT defects with Pareto analysis and root-cause recommendations. Shows defect distribution by type/severity, DPMO, and actionable fixes. Recognizes PCB/FPC-specific defect types including solder paste issues, tombstoning, bridging, missing components, cold joints, and pad lifting.
forecast_fab_load	semiops.read	Forecast fab electrical load for the next 24-168 hours using pattern-based model. Identifies peak/valley periods and demand response opportunities.
get_cleanroom_status	semiops.read	Get real-time cleanroom status including temperature, humidity, pressure, particle counts, and ISO compliance. Covers lamination rooms, PCB/FPC exposure zones, and general semiconductor cleanrooms. Use when asked about cleanroom conditions, environment, or contamination levels. Omit cleanroom_id to get all cleanrooms.
get_fab_pue	semiops.read	Get current Power Usage Effectiveness (PUE) for the fab facility with energy breakdown (IT load, cooling, lighting, HVAC, etc.) and benchmark rating. Applicable to PCB/FPC factories, semiconductor fabs, and electronics manufacturing plants. Use for energy efficiency questions.
get_filter_life	semiops.read	Get HEPA/ULPA filter remaining life prediction based on pressure drop trends. Shows estimated days remaining before filter replacement is needed. Covers cleanroom filters for PCB exposure areas, lamination zones, and semiconductor fabs. Do NOT use for predictive maintenance/mobile-equipment filter components such as excavators, loaders, generators, or customer fleet fleet assets.
get_iso_compliance	semiops.read	Get ISO 14644 compliance status and assessment history for cleanrooms. Shows current classification, pass/fail status, and historical assessment trends.
get_particle_trend	semiops.read	Get particle count trends over time for a specific cleanroom. Shows how particle levels have changed and helps identify contamination events or degradation patterns.
get_pressure_gradient	semiops.read	Get pressure gradient cascade status across cleanroom pairs. Shows whether pressure differentials between rooms are maintained correctly to prevent cross-contamination.
get_smt_oee	semiops.read	Get SMT (Surface Mount Technology) production line OEE (Overall Equipment Effectiveness) with Availability × Performance × Quality breakdown. Covers PCB assembly lines including solder paste printing, pick-and-place, reflow oven, and AOI stations. Use for production efficiency questions.
get_utility_status	semiops.read	Get utility systems status including CDA (Clean Dry Air), N2 (Nitrogen), PCW (Process Cooling Water), and UPW (Ultra Pure Water). Shows pressure, flow, purity readings.
monitor_particles	semiops.read	Monitor real-time particle counts in a cleanroom against ISO 14644-1 limits. Returns per-size evaluation, alerts for threshold exceedances, and overall status.
optimize_chiller_cop	semiops.read	Optimize chiller loading across multiple units to maximize system COP. Compares optimal vs equal-loading strategies and calculates energy savings.
predict_env_trend	semiops.read	Predict environmental parameter trends (temperature, humidity, particles) for the next 2-4 hours in a cleanroom. Use for proactive monitoring and early warning.
run_soft_sensors	semiops.read	Run virtual soft sensors to estimate unmeasurable parameters (AMC molecular contamination in ppb, dew point °C, HEPA filter loading %) from available cleanroom sensor data. Use when asked about molecular contamination, AMC levels, dew point, or filter status and no direct measurement is available.
simulate_smt_bottleneck	semiops.read	Run discrete-event simulation of an SMT production line to identify throughput bottleneck station, utilization imbalances, and optimization opportunities.

analyze_env_correlation · semiops.read

Analyze correlation between environmental parameters (temperature, humidity, pressure, particles) in a cleanroom. Helps identify which parameters influence each other. Especially useful for diagnosing temperature/humidity impact on solder paste performance, PCB lamination quality, and photolithography exposure accuracy.

Parameters

Parameter	Type	Required	Description
cleanroom_id	string	yes	Cleanroom ID to analyze

classify_smt_defects · semiops.read

Classify SMT defects with Pareto analysis and root-cause recommendations. Shows defect distribution by type/severity, DPMO, and actionable fixes. Recognizes PCB/FPC-specific defect types including solder paste issues, tombstoning, bridging, missing components, cold joints, and pad lifting.

Parameters

Parameter	Type	Required	Description
line_id	string		SMT line ID (optional, all lines if omitted)
days	integer		Days of defect data to analyze - default: 7

forecast_fab_load · semiops.read

Forecast fab electrical load for the next 24-168 hours using pattern-based model. Identifies peak/valley periods and demand response opportunities.

Parameters

Parameter	Type	Required	Description
hours_ahead	integer		Hours to forecast (1-168) - default: 24

get_cleanroom_status · semiops.read

Get real-time cleanroom status including temperature, humidity, pressure, particle counts, and ISO compliance. Covers lamination rooms, PCB/FPC exposure zones, and general semiconductor cleanrooms. Use when asked about cleanroom conditions, environment, or contamination levels. Omit cleanroom_id to get all cleanrooms.

Parameters

Parameter	Type	Required	Description
cleanroom_id	string		Cleanroom ID (omit for all cleanrooms)

get_fab_pue · semiops.read

Get current Power Usage Effectiveness (PUE) for the fab facility with energy breakdown (IT load, cooling, lighting, HVAC, etc.) and benchmark rating. Applicable to PCB/FPC factories, semiconductor fabs, and electronics manufacturing plants. Use for energy efficiency questions.

Parameters

No declared parameters. Discover live details at runtime through tools/list.

get_filter_life · semiops.read

Get HEPA/ULPA filter remaining life prediction based on pressure drop trends. Shows estimated days remaining before filter replacement is needed. Covers cleanroom filters for PCB exposure areas, lamination zones, and semiconductor fabs. Do NOT use for predictive maintenance/mobile-equipment filter components such as excavators, loaders, generators, or customer fleet fleet assets.

Parameters

Parameter	Type	Required	Description
cleanroom_id	string		Cleanroom ID (omit for all filters)

get_iso_compliance · semiops.read

Get ISO 14644 compliance status and assessment history for cleanrooms. Shows current classification, pass/fail status, and historical assessment trends.

Parameters

Parameter	Type	Required	Description
cleanroom_id	string		Cleanroom ID (omit for all)

get_particle_trend · semiops.read

Get particle count trends over time for a specific cleanroom. Shows how particle levels have changed and helps identify contamination events or degradation patterns.

Parameters

Parameter	Type	Required	Description
cleanroom_id	string	yes	Cleanroom ID to query
hours	integer		Hours of history to retrieve - default: 24
particle_size	string		Particle size filter (e.g. '0.5um', '5.0um')

get_pressure_gradient · semiops.read

Get pressure gradient cascade status across cleanroom pairs. Shows whether pressure differentials between rooms are maintained correctly to prevent cross-contamination.

Parameters

No declared parameters. Discover live details at runtime through tools/list.

get_smt_oee · semiops.read

Get SMT (Surface Mount Technology) production line OEE (Overall Equipment Effectiveness) with Availability × Performance × Quality breakdown. Covers PCB assembly lines including solder paste printing, pick-and-place, reflow oven, and AOI stations. Use for production efficiency questions.

Parameters

Parameter	Type	Required	Description
line_id	string		SMT line ID (omit for all lines)

get_utility_status · semiops.read

Get utility systems status including CDA (Clean Dry Air), N2 (Nitrogen), PCW (Process Cooling Water), and UPW (Ultra Pure Water). Shows pressure, flow, purity readings.

Parameters

No declared parameters. Discover live details at runtime through tools/list.

monitor_particles · semiops.read

Monitor real-time particle counts in a cleanroom against ISO 14644-1 limits. Returns per-size evaluation, alerts for threshold exceedances, and overall status.

Parameters

Parameter	Type	Required	Description
cleanroom_id	string	yes	Cleanroom ID to monitor

optimize_chiller_cop · semiops.read

Optimize chiller loading across multiple units to maximize system COP. Compares optimal vs equal-loading strategies and calculates energy savings.

Parameters

Parameter	Type	Required	Description
cooling_demand_kw	number	yes	Total cooling demand in kW
ambient_temp_c	number		Outdoor ambient temperature °C - default: 35

predict_env_trend · semiops.read

Predict environmental parameter trends (temperature, humidity, particles) for the next 2-4 hours in a cleanroom. Use for proactive monitoring and early warning.

Parameters

Parameter	Type	Required	Description
cleanroom_id	string	yes	Cleanroom ID to predict
parameter	string		Environmental parameter to predict - one of: temperature, humidity, particles, pressure
hours	integer		Hours ahead to predict (1-8) - default: 4

run_soft_sensors · semiops.read

Run virtual soft sensors to estimate unmeasurable parameters (AMC molecular contamination in ppb, dew point °C, HEPA filter loading %) from available cleanroom sensor data. Use when asked about molecular contamination, AMC levels, dew point, or filter status and no direct measurement is available.

Parameters

Parameter	Type	Required	Description
temperature_c	number		Current cleanroom temperature in °C
humidity_pct	number		Current relative humidity %
particle_05um	number		Current 0.5 µm particle count, particles/m³
air_changes_hour	integer		Air changes per hour (ACH) - default: 600
cleanroom_age_days	integer		Age of the cleanroom in days (affects outgassing AMC estimate) - default: 365
filter_dp_pa	number		Current HEPA/ULPA filter pressure drop in Pa - default: 200
filter_initial_dp_pa	number		Pressure drop of a new clean filter in Pa - default: 50
filter_max_dp_pa	number		Replacement-threshold filter pressure drop in Pa - default: 450
filter_operating_hours	number		Cumulative filter operating hours - default: 4380

simulate_smt_bottleneck · semiops.read

Run discrete-event simulation of an SMT production line to identify throughput bottleneck station, utilization imbalances, and optimization opportunities.

Parameters

Parameter	Type	Required	Description
line_id	string		SMT line ID (optional, uses default config)
sim_hours	number		Simulation duration in hours - default: 8
boards	integer		Number of boards to produce - default: 500

Aviation reliability analysis

Endpoint: /mcp/aviation/

Tool	Scope	Description
aviation_component_compare	aviation.analysis.read	Compare reliability behavior across component groups.
aviation_fault_query	aviation.data.read	原始故障记录明细查询(按机型/机号/基地/ATA/关键字/来源/时间过滤,分页)
aviation_fleet_stats	aviation.analysis.read	Summarize fleet reliability signals by ATA chapter and failure distribution.
aviation_fleet_utilization_query	aviation.data.read	机队利用率明细(飞行小时/起落/在册数),分页
aviation_kpi_attribution	aviation.analysis.read	Attribute a reliability KPI to supporting failure records and evidence.
aviation_kpi_monthly_query	aviation.data.read	官方月度 KPI 明细(aviation_kpi_monthly,只读),分页
aviation_removal_query	aviation.data.read	原始拆换记录明细(aviation_fact_removal),分页
aviation_repetitive_fault_detect	aviation.analysis.read	Detect and summarize repetitive fault groups for reliability review.
aviation_risk_register_query	aviation.data.read	风险登记册明细(aviation_risk_register),分页
aviation_text_mining_scan	aviation.analysis.read	Scan maintenance text for recurring or unusual technical issue candidates.
aviation_weibull_fit	aviation.analysis.read	Fit Weibull reliability curves for selected replacement or removal data.

aviation_component_compare · aviation.analysis.read

Compare reliability behavior across component groups.

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
cohort	string		Comparison cohort. - one of: ORIGINAL_VS_REPAIR - default: "ORIGINAL_VS_REPAIR"
method	string		Weibull fitting method for both cohorts. - one of: MLE, LSM - default: "MLE"
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

aviation_fault_query · aviation.data.read

原始故障记录明细查询(按机型/机号/基地/ATA/关键字/来源/时间过滤,分页)

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
unit	string		Alias for base.
aircraftReg	string		Aircraft registration.
aircraftNo	string		Alias for aircraftReg.
ataChapter	string		ATA chapter filter.
ataSection	string		ATA section filter.
keyword	string		LIKE filter over fault description and message code.
source	string		Fault source, for example FTS or APCM.
dateFrom	string		Inclusive occurrence date start, yyyy-MM-dd.
dateTo	string		Exclusive occurrence date end, yyyy-MM-dd.
page	integer		Zero-based page number. - default: 0
pageSize	integer		Rows per page; server clamps to 1..500. - default: 50
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

aviation_fleet_stats · aviation.analysis.read

Summarize fleet reliability signals by ATA chapter and failure distribution.

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

aviation_fleet_utilization_query · aviation.data.read

机队利用率明细(飞行小时/起落/在册数),分页

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
page	integer		Zero-based page number. - default: 0
pageSize	integer		Rows per page; server clamps to 1..500. - default: 50
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

aviation_kpi_attribution · aviation.analysis.read

Attribute a reliability KPI to supporting failure records and evidence.

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
kpiCode	string	yes	KPI code to attribute, for example mech_sdr_rate.
yearMonths	array	yes	Months to inspect, formatted YYYY-MM.
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

aviation_kpi_monthly_query · aviation.data.read

官方月度 KPI 明细(aviation_kpi_monthly,只读),分页

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
page	integer		Zero-based page number. - default: 0
pageSize	integer		Rows per page; server clamps to 1..500. - default: 50
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

aviation_removal_query · aviation.data.read

原始拆换记录明细(aviation_fact_removal),分页

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
page	integer		Zero-based page number. - default: 0
pageSize	integer		Rows per page; server clamps to 1..500. - default: 50
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

aviation_repetitive_fault_detect · aviation.analysis.read

Detect and summarize repetitive fault groups for reliability review.

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
state	string		Repetitive-fault queue state. - default: "PENDING"
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

aviation_risk_register_query · aviation.data.read

风险登记册明细(aviation_risk_register),分页

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
page	integer		Zero-based page number. - default: 0
pageSize	integer		Rows per page; server clamps to 1..500. - default: 50
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

aviation_text_mining_scan · aviation.analysis.read

Scan maintenance text for recurring or unusual technical issue candidates.

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

aviation_weibull_fit · aviation.analysis.read

Fit Weibull reliability curves for selected replacement or removal data.

Parameters

Parameter	Type	Required	Description
base	string		Maintenance base/unit filter.
aircraftType	string		Single aircraft type filter, for example A320 or B737NG.
aircraftTypes	array		Aircraft type group; use only when the tool supports merged scopes.
ataChapters	array		ATA chapter filters, for example ['27', '32'].
partNumber	string		Component part number filter for reliability/component tools.
fromDate	string		Inclusive ISO date start for the data scope, yyyy-MM-dd.
toDate	string		Inclusive ISO date end for the data scope, yyyy-MM-dd.
criticalOnly	boolean		Limit to critical issues where the aviation module supports it.
method	string		Weibull fitting method. - one of: MLE, LSM - default: "MLE"
rightCensoringEnabled	boolean		Include right-censored observations. - default: true
dataScope	object		Compatibility envelope accepted by older clients. Prefer direct fields.
params	object		Compatibility envelope accepted by older clients. Prefer direct fields.

JSON reference: tools.json