code/mcp_servers/README.md

metadata

status: canonical-index
scope: team-repo
owner: Team 13
canonical: true

mcp_servers/

MCP (Model Context Protocol) servers wrapping the four AssetOpsBench tool domains. Each subdirectory is a standalone MCP server that can be launched independently or composed into a multi-server agent pipeline.

Architecture

All four servers import shared data-loading helpers from base.py, which points at data/processed/. Each server exposes a set of tools via the MCP JSON-RPC interface.

mcp_servers/
├── base.py                 # shared data loader + utilities
├── iot_server/             # asset metadata + sensor readings
│   └── server.py           # tools: list_assets, get_asset_metadata, list_sensors, get_sensor_readings
├── fmsr_server/            # failure mode to sensor relation
│   └── server.py           # tools: list_failure_modes, search_failure_modes, get_sensor_correlation,
│                           #        get_dga_record, analyze_dga (IEC 60599 Rogers Ratio)
├── tsfm_server/            # time-series forecasting + RUL
│   └── server.py           # tools: get_rul, forecast_rul, detect_anomalies (z-score),
│                           #        trend_analysis (OLS)
└── wo_server/              # work order management
    └── server.py           # tools: list/get fault records, create/list/update work orders,
                            #        estimate_downtime

Running a server

# From repo root, with the team .venv active:
python -m mcp_servers.iot_server.server

In practice, the benchmark path composes multiple servers at once; these modules are intentionally independent so the harness can start only the domains it needs. The agent or harness layer, not the server, is responsible for multi-turn orchestration across domains.

Design notes

• Shared loader layer keeps data loading DRY — schema changes in data/processed/ only need updating in base.py.
• Stateless tool calls — servers don't maintain session state; the agent holds multi-turn context.
• No network side effects — all read paths come from local CSVs. The only write path is the in-memory WO session store used for work-order creation during a run.
• Real domain logic, not stubs — e.g. fmsr_server.analyze_dga implements the IEC 60599 Rogers Ratio method for dissolved gas analysis, not a dummy return.

Status (Apr 7, 2026)

• Skeletons landed for all four domains (commit 717e9b4, Anonymous reviewer)
• Substantive domain logic implemented (Rogers Ratio, RUL forecast, anomaly detection, work-order CRUD)
• In progress (W2): hardening, unit tests, integration with the AssetOpsBench evaluation harness