mcp-pinecone

Overview

This MCP server provides a bridge between Claude Desktop and Pinecone vector databases, allowing AI assistants to interact with vector data. It implements several key tools including semantic search across indexed documents, document processing with chunking and embedding, and statistics retrieval about the vector index. The server uses Pinecone's inference API for embeddings and integrates cleanly with Claude's MCP architecture.

Try asking AI

After installing, here are 3 things you can ask your AI assistant:

• semantic-search

  Search for records in the Pinecone index.

  Try:Find documents similar to 'machine learning best practices'Try:Search for information about neural networks
• read-document

  Read a document from the Pinecone index.

  Try:Retrieve document with ID 'doc123'Try:Get content of document 'research-paper-01'
• list-documents

  List all documents in the Pinecone index.

  Try:Show all documents in the indexTry:List all available document IDs
• pinecone-stats

  Get stats about the Pinecone index, including number of records, dimensions, and namespaces.

  Try:Show me index statisticsTry:Get information about the Pinecone database size
• process-document

  Process a document into chunks and upsert them into the Pinecone index.

  Try:Process this document and add it to the indexTry:Upload and chunk a research paper

Comparable tools

Installation

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

uvx install mcp-pinecone

uv pip install mcp-pinecone

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

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

Compare mcp-pinecone with