bird-id-mcp

Bird species identification MCP server. YOLO detection + ConvNeXt classification, outputs Top-5 species with confidence and Chinese names.

Install & Run

# Run directly with uvx (auto-installs)
uvx bird-id-mcp

# Or install from git
pip install git+https://github.com/Hakureirm/bird-id-mcp.git
bird-id-mcp

Models are automatically downloaded from HuggingFace on first run (~50MB default).

Model Selection

Model	Size	Speed (x86 1T)	Accuracy
S1v2 (default)	37MB	~150ms	Good
ConvNeXt	144MB	~600ms	Best

Default is S1v2 (fast + small). To use ConvNeXt:

BIRD_ID_CLS_MODEL=convnext uvx --from git+https://github.com/Hakureirm/bird-id-mcp.git bird-id-mcp

Claude Desktop / Agent Config

{
  "mcpServers": {
    "bird-id": {
      "command": "uvx",
      "args": ["bird-id-mcp"]
    }
  }
}

Tools

identify_bird

Identify bird species from an image file path.

Input:  {"image_path": "/path/to/bird.jpg", "topk": 5}
Output: {
  "detections": 1,
  "detection_confidence": 0.92,
  "bbox": {"x1": 100, "y1": 50, "x2": 400, "y2": 350},
  "results": [
    {"rank": 1, "species": "Little Egret", "species_cn": "白鹭", "confidence": 78.5},
    {"rank": 2, "species": "Snowy Egret", "species_cn": "雪鹭", "confidence": 12.3},
    ...
  ]
}

identify_bird_base64

Same as above but accepts base64-encoded image data.

Models

• Detection: YOLOv8 bird detector (12MB ONNX)
• Classification: S1v2 (37MB, default) or ConvNeXt-Tiny (144MB), 10,753 bird species
• Taxonomy: eBird species info — scientific name, family, order, description
• Inference: ONNX Runtime CPU only, no GPU required