Pinecone Developer MCP Server

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Pinecone's official MCP server connects AI assistants to Pinecone vector databases for retrieval-augmented generation (RAG) workflows. Lets agents create and configure indexes, upsert and embed documents, and run semantic searches over vector data — all from natural language, without leaving the editor or chat.

Connect

Add this configuration to .claude/mcp.json

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": [
        "-y",
        "@pinecone-database/mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "<YOUR_PINECONE_API_KEY>"
      }
    }
  }
}

Tools (2)

search-records

Run a semantic search over a Pinecone index and return the most relevant records.

{
  "type": "object",
  "required": [
    "index_name",
    "query"
  ],
  "properties": {
    "query": {
      "type": "string",
      "description": "Natural-language query to search for"
    },
    "top_k": {
      "type": "integer",
      "description": "Number of results to return"
    },
    "index_name": {
      "type": "string",
      "description": "Name of the Pinecone index to query"
    }
  }
}

upsert-records

Insert or update records (with text to embed) into a Pinecone index.

{
  "type": "object",
  "required": [
    "index_name",
    "records"
  ],
  "properties": {
    "records": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Records to upsert, each with an id and text/metadata"
    },
    "index_name": {
      "type": "string",
      "description": "Name of the target Pinecone index"
    }
  }
}

Resources

This server does not expose any resources.

Prompts

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Server Information

Author
Pinecone (@pinecone-io)
Repository
https://github.com/pinecone-io/pinecone-mcp
License
MIT
Stars
0
Last Updated
June 12, 2026