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 step1

Config MCP Setting step2

Enable MCP step3

Chat with MCP

Example: Searching for images based on given text text_2_image

<div style="display: flex; gap: 10px;"> <img src="https://p0.qhimg.com/t11098f6bcd000b4fb05d7bf627.jpg" alt="Image 1" title="https://p0.qhimg.com/t11098f6bcd000b4fb05d7bf627.jpg" style="width: 45%;"> <img src="https://p0.qhimg.com/t11098f6bcdc3c5f3e99a1dbfad.jpg" alt="Image 2" title="https://p0.qhimg.com/t11098f6bcdc3c5f3e99a1dbfad.jpg" style="width: 45%;"> </div>

Image URLs:

• https://p0.qhimg.com/t11098f6bcd000b4fb05d7bf627.jpg
• https://p0.qhimg.com/t11098f6bcdc3c5f3e99a1dbfad.jpg

License

Apache License 2.0