Bird species identification MCP server — YOLO detection + ConvNeXt classification, Top5 output
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# bird-id-mcp Bird species identification MCP server. YOLO detection + ConvNeXt classification, outputs Top-5 species with confidence and Chinese names. ## Install & Run ```bash # 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: ```bash BIRD_ID_CLS_MODEL=convnext uvx --from git+https://github.com/Hakureirm/bird-id-mcp.git bird-id-mcp ``` ## Claude Desktop / Agent Config ```json { "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
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
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