motor-telemetry-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@motor-telemetry-mcpWhat's the condition of motor MTR-003?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Industrial Motor Telemetry — MCP Server (PoC)
A Proof of Concept that exposes an industrial motor telemetry system to an AI agent through the Model Context Protocol (MCP), while also serving a conventional FastAPI REST interface — both backed by the same async service layer.
Why this design
Concern | Where it lives | Rationale |
Data contract |
| Single source of truth, validated by Pydantic v2. |
Business logic |
| Transport-agnostic async logic; no FastAPI/MCP imports → unit-testable and reusable. |
Transports |
| MCP tool and REST endpoint both delegate to the one service coroutine — zero logic duplication. |
The MCP server (built on the official mcp SDK's MCPServer) is served over
Streamable HTTP and mounted onto the FastAPI app, so a single uvicorn
process exposes both protocols.
Related MCP server: thingworx-mcp
Requirements
Python 3.10+
Dependencies in
requirements.txt(FastAPI, Uvicorn, Pydantic v2,mcpv2)
Run locally
cd motor-telemetry-mcp
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txt
# Start the combined REST + MCP server
uvicorn main:app --reload --port 8000MCP endpoint (Streamable HTTP):
http://localhost:8000/mcpREST docs (Swagger UI):
http://localhost:8000/docs
Try the REST API
curl http://localhost:8000/motors # list motor IDs
curl http://localhost:8000/motors/MTR-001 # NORMAL
curl http://localhost:8000/motors/MTR-003 # CRITICAL (overheating)
curl -i http://localhost:8000/motors/MTR-999 # 404 with clear detailRun the tests
pip install -r requirements-dev.txt
pytestThe suite (14 tests) covers all three layers with no network sockets:
tests/test_service.py— async domain logic (health mapping, forgiving input normalization, not-found error).tests/test_rest_api.py— FastAPI endpoints via httpx's in-process ASGI transport (200s, structured body, 404).tests/test_mcp_tool.py— MCP layer in-process: tool discovery, input schema, structured output, and the tool-error path.
Run with Docker
docker build -t motor-telemetry-mcp .
docker run --rm -p 8000:8000 motor-telemetry-mcpThe image is slim, installs deps in a cached layer, runs as a non-root
user, and ships a container HEALTHCHECK against /health.
Mock fleet
Motor ID | Temp (°C) | Vibration (mm/s) | RPM | Status |
| 62.5 | 1.8 | 1490 |
|
| 83.0 | 4.2 | 1475 |
|
| 118.4 | 11.6 | 1360 |
|
How an AI agent connects to the MCP tool
The server advertises one tool, get_motor_status_tool, and one resource,
motor://fleet. An MCP-capable agent runtime discovers and invokes it exactly
like the included client_example.py:
# In a second terminal, with the server running:
python client_example.pyConceptual flow:
Connect — the agent's MCP client opens a Streamable-HTTP session to
http://localhost:8000/mcp.Discover — it calls
list_tools()and receives the schema forget_motor_status_tool(auto-generated from the Python type hints).Invoke — when the user asks "Is motor MTR-003 healthy?", the LLM emits a tool call
get_motor_status_tool(motor_id="MTR-003").Reason — the server returns structured JSON (temperature, vibration, rpm, status). The agent reads
status: "CRITICAL"and can respond / escalate. Unknown motors return an MCP tool error result, which the agent can surface gracefully.
Wiring it into a real client (e.g. Claude Desktop / any MCP host)
Point the host at the Streamable-HTTP URL. Example host config entry:
{
"mcpServers": {
"motor-telemetry": {
"type": "http",
"url": "http://localhost:8000/mcp"
}
}
}Project layout
motor-telemetry-mcp/
├── models.py # Pydantic models: MotorStatus, MotorHealth enum
├── telemetry_service.py # Async, transport-agnostic domain logic + mock data
├── main.py # FastAPI app + MCP server (Streamable HTTP) mounted
├── client_example.py # Demo MCP client that calls the tool
├── tests/ # pytest suite (service, REST, MCP layers)
├── Dockerfile # Slim, non-root container image
├── .dockerignore
├── pytest.ini
├── requirements.txt
├── requirements-dev.txt # test/dev dependencies
└── README.mdProduction notes (talking points)
Swap
telemetry_serviceinternals for a real historian / OPC-UA / time-series source — no transport code changes needed.stateless_http=Truekeeps the MCP transport horizontally scalable.Add auth (the
mcpSDK supports token verifiers / OAuth resource servers) and rate limiting before exposing beyond localhost.
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