Mcp Pinecone

What is Mcp Pinecone?

Mcp Pinecone provides Model Context Protocol endpoints that let a client (e.g., Claude Desktop) interact with a Pinecone vector index. It supports reading, writing, searching, and utility operations such as stats and document processing.

How to use Mcp Pinecone?

1. Install the package with UV (or UVX):

   uvx install mcp-pinecone   # or uv pip install mcp-pinecone
   

2. Configure the server in Claude Desktop’s claude_desktop_config.json:

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

3. Run Claude Desktop; it will launch the MCP server via the command defined above.
4. Use the built‑in tools (semantic-search, read-document, list-documents, etc.) from the Claude UI to interact with your Pinecone index.

Key Features

• Semantic Search – query the index with natural language, embeddings generated via Pinecone’s inference API.
• Document Management – read, list, and upsert documents; automatic chunking and embedding.
• Index Stats – retrieve dimensions, record count, namespaces, and other metadata.
• Tool‑Based Architecture – each operation exposed as an MCP tool, callable from Claude Desktop.
• Fast Installation – single‑command install via UV/UVX; optional Smithery one‑click install.
• Debugging Support – works with the MCP Inspector for step‑by‑step debugging.

Use Cases

• RAG (Retrieval‑Augmented Generation) – feed relevant chunks from Pinecone into Claude for context‑aware generation.
• Knowledge Base – store internal documents, policies, or FAQs and retrieve them semantically.
• Search‑Powered Apps – build custom applications that query Pinecone through Claude’s UI.
• Analytics Dashboard – monitor index health and usage via the pinecone-stats tool.

FAQ

Q: Do I need a Pinecone account? A: Yes. Sign up at pinecone.io, create an index, and obtain an API key.

Q: Which embedding model is used? A: Embeddings are generated via Pinecone’s built‑in inference API.

Q: Can I run the server locally without Claude? A: The server runs over stdio and expects MCP‑compatible clients; you can test it with the MCP Inspector.

Q: How are documents chunked? A: A token‑based chunker similar to LangChain splits documents before embedding.

Q: How do I publish updates to PyPI? A: Use uv build to create distributions and uv publish with your PyPI token.

Pinecone Model Context Protocol Server for Claude Desktop.

Read and write to a Pinecone index.

Components

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.