Pinecone Model Context Protocol Server For Claude Desktop.

What is MCP-Pinecone?

MCP-Pinecone is a Model Context Protocol server designed to facilitate reading and writing operations from Pinecone, a vector database service. This server provides a basic Retrieval-Augmented Generation (RAG) capability, allowing developers to seamlessly integrate advanced AI functionalities into their applications. By leveraging MCP-Pinecone, users can enhance their data retrieval processes, making it easier to access and utilize information stored in Pinecone.

Features of MCP-Pinecone

• Integration with Pinecone: Directly connects to Pinecone, enabling efficient data management and retrieval.
• RAG Capabilities: Supports Retrieval-Augmented Generation, enhancing the quality of generated responses by utilizing external data.
• Open Source: Available for public use, allowing developers to contribute and modify the code as needed.
• User-Friendly Interface: Designed with ease of use in mind, making it accessible for both novice and experienced developers.
• Active Community: Backed by a community of contributors, ensuring continuous improvement and support.

How to Use MCP-Pinecone

1. Installation: Clone the repository from GitHub and install the necessary dependencies.

   git clone https://github.com/sirmews/mcp-pinecone.git
   cd mcp-pinecone
   npm install
   

2. Configuration: Set up your Pinecone API key and other configurations in the environment variables or configuration files.

3. Running the Server: Start the server to begin interacting with Pinecone.

   npm start
   

4. Making Requests: Use the provided API endpoints to read and write data to Pinecone. Refer to the documentation for detailed instructions on available endpoints and their usage.

5. Contributing: If you wish to contribute to the project, feel free to fork the repository, make your changes, and submit a pull request.

Frequently Asked Questions

What is Pinecone?

Pinecone is a fully managed vector database designed for machine learning applications. It allows users to store and query high-dimensional vectors efficiently, making it ideal for applications involving AI and data science.

How does MCP-Pinecone enhance data retrieval?

By implementing Retrieval-Augmented Generation (RAG), MCP-Pinecone allows applications to generate responses based on both the model's training and real-time data from Pinecone, leading to more accurate and contextually relevant outputs.

Is MCP-Pinecone free to use?

Yes, MCP-Pinecone is an open-source project, and you can use it for free. However, using Pinecone may incur costs based on your usage of their services.

Can I contribute to the MCP-Pinecone project?

Absolutely! Contributions are welcome. You can fork the repository, make improvements, and submit a pull request to share your enhancements with the community.

Where can I find the documentation for MCP-Pinecone?

Documentation is typically included in the repository. You can also check the README file for setup instructions and usage guidelines.

Pinecone Model Context Protocol Server for Claude Desktop.

Read and write to a Pinecone index.

Components

flowchart TB
    subgraph Client["MCP Client (e.g., Claude Desktop)"]
        UI[User Interface]
    end

    subgraph MCPServer["MCP Server (pinecone-mcp)"]
        Server[Server Class]
        
        subgraph Handlers["Request Handlers"]
            ListRes[list_resources]
            ReadRes[read_resource]
            ListTools[list_tools]
            CallTool[call_tool]
            GetPrompt[get_prompt]
            ListPrompts[list_prompts]
        end
        
        subgraph Tools["Implemented Tools"]
            SemSearch[semantic-search]
            ReadDoc[read-document]
            ListDocs[list-documents]
            PineconeStats[pinecone-stats]
            ProcessDoc[process-document]
        end
    end

    subgraph PineconeService["Pinecone Service"]
        PC[Pinecone Client]
        subgraph PineconeFunctions["Pinecone Operations"]
            Search[search_records]
            Upsert[upsert_records]
            Fetch[fetch_records]
            List[list_records]
            Embed[generate_embeddings]
        end
        Index[(Pinecone Index)]
    end

    %% Connections
    UI --> Server
    Server --> Handlers
    
    ListTools --> Tools
    CallTool --> Tools
    
    Tools --> PC
    PC --> PineconeFunctions
    PineconeFunctions --> Index
    
    %% Data flow for semantic search
    SemSearch --> Search
    Search --> Embed
    Embed --> Index
    
    %% Data flow for document operations
    UpsertDoc --> Upsert
    ReadDoc --> Fetch
    ListRes --> List

    classDef primary fill:#2563eb,stroke:#1d4ed8,color:white
    classDef secondary fill:#4b5563,stroke:#374151,color:white
    classDef storage fill:#059669,stroke:#047857,color:white
    
    class Server,PC primary
    class Tools,Handlers secondary
    class Index storage

Resources

The server implements the ability to read and write to a Pinecone index.

Tools

• semantic-search: Search for records in the Pinecone index.
• read-document: Read a document from the Pinecone index.
• list-documents: List all documents in the Pinecone index.
• pinecone-stats: Get stats about the Pinecone index, including the number of records, dimensions, and namespaces.
• process-document: Process a document into chunks and upsert them into the Pinecone index. This performs the overall steps of chunking, embedding, and upserting.

Note: embeddings are generated via Pinecone's inference API and chunking is done with a token-based chunker. Written by copying a lot from langchain and debugging with Claude.

Quickstart

Installing via Smithery

To install Pinecone MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install mcp-pinecone --client claude

Install the server

Recommend using uv to install the server locally for Claude.

uvx install mcp-pinecone

OR

uv pip install mcp-pinecone

Add your config as described below.

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Note: You might need to use the direct path to uv. Use which uv to find the path.

Development/Unpublished Servers Configuration

"mcpServers": {
  "mcp-pinecone": {
    "command": "uv",
    "args": [
      "--directory",
      "{project_dir}",
      "run",
      "mcp-pinecone"
    ]
  }
}

Published Servers Configuration

"mcpServers": {
  "mcp-pinecone": {
    "command": "uvx",
    "args": [
      "--index-name",
      "{your-index-name}",
      "--api-key",
      "{your-secret-api-key}",
      "mcp-pinecone"
    ]
  }
}

Sign up to Pinecone

You can sign up for a Pinecone account here.

Get an API key

Create a new index in Pinecone, replacing {your-index-name} and get an API key from the Pinecone dashboard, replacing {your-secret-api-key} in the config.

Development

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:

uv sync

2. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

3. Publish to PyPI:

uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

• Token: --token or UV_PUBLISH_TOKEN
• Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory {project_dir} run mcp-pinecone

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Source Code

The source code is available on GitHub.

Contributing

Send your ideas and feedback to me on Bluesky or by opening an issue.