lft-reader-mcp

An MCP server that lets an AI assistant read COVID-19 lateral flow tests from photographs.

It exposes a deployed computer-vision pipeline — YOLOv8-OBB for oriented cassette detection, MobileNetV3-Small for classification, and a classical OpenCV line counter for corroboration — as two tools any MCP client can call.

Research and demonstration use only. Not a medical device.

Why this exists

The model already runs as a FastAPI service on Google Cloud Run, which means a human can upload a photo and read the verdict. This server closes the last gap: it lets an AI assistant do the same thing as part of a larger task, without a person copying files between windows.

Claude / Cursor  ──MCP──►  this server  ──HTTPS──►  Cloud Run
                                                     ├── YOLOv8-OBB   (detect)
                                                     ├── MobileNetV3  (classify)
                                                     └── OpenCV       (count lines)

No model weights live here. The server is a thin, well-behaved client: it validates input before spending a network call, gives the cold start enough room to finish, and returns "no cassette found" as a result rather than an error, because a well-formed request that finds nothing is not a failure.

Related MCP server: deepseek-vision-mcp

Install

Windows (PowerShell)

py -m pip install -r requirements.txt
py test_server.py

macOS / Linux

python3 -m pip install -r requirements.txt
python3 test_server.py

py is the Windows Python launcher. Use it rather than python3, which on Windows is usually intercepted by a Microsoft Store shortcut.

SDK versions

Works with both major versions of the Python MCP SDK. Version 2.0 renamed FastMCP to MCPServer and Tool.inputSchema to Tool.input_schema; the server resolves the import at load time and the tests read either attribute, so there is no need to pin an old major version. Verified against mcp 2.2.0 and mcp 1.30.0.

Wire it into a client

Claude Desktop, Windows — %APPDATA%\Claude\claude_desktop_config.json. Backslashes must be doubled inside JSON:

{
  "mcpServers": {
    "lft-reader": {
      "command": "py",
      "args": ["C:\\Users\\YOURNAME\\lft-mcp\\server.py"]
    }
  }
}

Claude Desktop, macOS — ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "lft-reader": {
      "command": "python3",
      "args": ["/absolute/path/to/lft-mcp/server.py"]
    }
  }
}

Cursor — the same block in .cursor/mcp.json in your project.

Example files for both are in this repo.

Restart the client. Then ask it, in plain language:

Read the lateral flow test in ~/Desktop/test.jpg

Tools

read_lateral_flow_test(image_path)

Reads a test from a local image and returns the verdict.

{
  "detected": true,
  "result": "positive",
  "confidence": 0.9811,
  "decision_source": "cnn+lines agree (positive)",
  "detection_confidence": 0.9084,
  "processing_ms": 523.3
}

Send a photo of the test in a scene — on a table, in a hand. The detector was trained to find a cassette within a wider frame, so a tight crop of the cassette alone returns detected: false with a note saying so.

check_reader_health()

Reports whether the service is up and its models are loaded. Useful before a batch, and to tell a service problem apart from a problem with one photo.

Configuration

Point it at a local instance:

$env:LFT_API_BASE="http://localhost:8080"; py server.py    # Windows

LFT_API_BASE=http://localhost:8080 python3 server.py       # macOS / Linux

Known limitations

• Cold starts. The service scales to zero to stay inside the free tier, so the first call after ~15 minutes idle takes a few seconds while the models load. Warm calls are around 500 ms. The client allows 90 seconds before giving up.

• Pre-cropped images are not detected. See above — this is a property of how the detector was trained, not a bug.

• invalid is the weakest class. It had only 5 held-out examples and 50 training images.

• Accuracy figures for the underlying models: detector mAP50 0.993 (mAP50-95 0.857); classifier 96.3% on 81 held-out images — 60 positive, 16 negative, 5 invalid, macro-F1 0.898. The classifier number should always be read with that class imbalance in mind.

Tests

py test_server.py        # Windows
python3 test_server.py   # macOS / Linux

Checks that the server loads, declares both tools with usable descriptions and schemas, and rejects missing files, unsupported formats and empty files before making any network call.

Licence

MIT for this server. It contains no model weights; the underlying models and service are governed by their own licences (the detector is YOLOv8, AGPL-3.0).