FGCLIP-MCP

MCP (Model Context Protocol) server for FG-CLIP embedding services. To obtain and configure the API key, please apply at https://research.360.cn/sass.

Features

This MCP server provides the following tools and resources:

Tools

• text_embedding: Generate embedding vectors for text

• image_embedding: Generate embedding vectors for images

• cosine_similarity: Compute cosine similarity between two lists of vectors

Use Cases

This MCP server helps users achieve the following capabilities:

• Image Feature Extraction: Convert images into high-dimensional vector representations for machine learning and similarity computation

• Text Feature Extraction: Transform text into semantic vector representations with multi-language support

• Multi-modal Similarity Computation:

  • Image-to-Image Similarity: Compare visual similarity between different images

  • Image-to-Text Similarity: Enable cross-modal retrieval, such as finding relevant images based on text descriptions

  • Text-to-Text Similarity: Calculate semantic similarity between texts

Through these capabilities, users can build powerful search engines, recommendation systems, content classification, and multi-modal AI applications.

Tool Details

text_embedding

Generate embedding vectors for input texts.

Parameters:

• texts: A list of text strings to embed

• model: The model to use (default: "fg-clip")

Returns:

• saved_uris: A list of URIs where the embeddings are stored

• success: Whether the operation succeeded

• error_msg: Error message, if any

image_embedding

Generate embedding vectors for images.

Parameters:

• images: A list of image URLs or base64-encoded images

• model: The model to use (default: "fg-clip")

Returns:

• saved_uris: A list of URIs where the embeddings are stored

• success: Whether the operation succeeded

• error_msg: Error message, if any

cosine_similarity

Compute cosine similarity between two lists of vectors.

Parameters:

• uris_a: A list of URIs for the first set of embeddings

• uris_b: A list of URIs for the second set of embeddings

• mode: Calculation mode (default: "pairwise")

  • "pairwise": Compute similarity for vectors at corresponding positions

  • "matrix": Compute a full similarity matrix for all vector pairs

Returns:

• similarities: Similarity values or a similarity matrix

• shape: Shape information of the result

• success: Whether the operation succeeded

Development & Testing

git clone https://github.com/360CVGroup/FGCLIP-MCP 
cd FGCLIP-MCP
uv venv
uv sync
source .venv/bin/activate
export MCP_API_KEY=your_api_key 
pytest -q

MCP Host Configuration

From pypi

{
  "mcpServers": {  
    "fgclip-mcp": {
      "command": "uvx",
      "args": [
        "fgclip-mcp"
      ],
      "env": {
        "MCP_API_KEY": "your_api_key"
      }
    }
  }
}

From local

{
  "mcpServers": {  
    "fgclip-mcp-local": {
      "command": "uv",
      "args": [
        "--directory",
        "/path_to_fgclip-mcp/src/fgclip_mcp",
        "run",
        "/path_to_fgclip-mcp/src/fgclip_mcp/__main__.py"
      ],
      "env": {
        "MCP_API_KEY": "your_api_key"
      }    
    }
  }
}

Use Case in Cursor IDE

Locate MCP Setting

Config MCP Setting

Enable MCP

Chat with MCP

Example: Searching for images based on given text

Image URLs:

• https://p0.qhimg.com/t11098f6bcd000b4fb05d7bf627.jpg

• https://p0.qhimg.com/t11098f6bcdc3c5f3e99a1dbfad.jpg

License

Apache License 2.0