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Pinecone MCP

Connect AI agents to Pinecone vector databases

Vector DatabaseFreeClaudeClaude CodeCursorWindsurfAll ModelsEasy✓ Verified
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Last verified: September 20, 2026

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Maintenance status

Active
Last code update: September 13, 2026License: Apache-2.0Checked September 28, 2026

How to Install Pinecone MCP

$npx -y @pinecone-database/mcp

Compatible with: Claude, Claude Code, Cursor, Windsurf, All Models.

Setup by client

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "pinecone-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@pinecone-database/mcp"
      ]
    }
  }
}

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "pinecone-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@pinecone-database/mcp"
      ]
    }
  }
}

VS Code (.vscode/mcp.json)

{
  "servers": {
    "pinecone-mcp": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@pinecone-database/mcp"
      ]
    }
  }
}

Claude Code (terminal)

claude mcp add pinecone-mcp -- npx -y @pinecone-database/mcp

Add any API keys or environment variables the server's README lists.

About Pinecone MCP

The official Pinecone MCP server lets AI agents and coding assistants perform vector database operations directly: searching documentation, describing index stats, creating indexes, and running semantic search for RAG. Available as a managed remote endpoint or a self-hosted local server.

Pinecone MCP is a Vector Database MCP server designed for developers and AI power users. It enables extending AI assistants with real-world capabilities by exposing a structured set of tools over the Model Context Protocol. Key capabilities include standards-based mcp server interface, works with claude, gpt, gemini, and other mcp clients, tool discovery and structured outputs, and structured tool calls that work cleanly inside an LLM context. It integrates with Claude Desktop, Cursor, Windsurf, and any MCP-compatible client, and is best suited for developers and AI power users who need ai engineers wiring assistants into real tools.

Key Features

  • vector-database
  • rag
  • semantic-search
  • Standards-based MCP server interface
  • Works with Claude, GPT, Gemini, and other MCP clients
  • Tool discovery and structured outputs

Pricing

Free: from Free (open source; requires a Pinecone account)

Pros & Cons

Pros

  • Drop-in support across MCP-compatible AI clients
  • Reduces prompt boilerplate by exposing concrete tool calls
  • Active community and ongoing updates

Cons

  • Requires an MCP-capable client to use
  • Initial setup can require configuration
  • Capability surface depends on what the underlying service exposes

Best For

  • AI engineers wiring assistants into real tools
  • Teams adopting MCP as an integration standard
  • Power users who want more than chat

Our Take on Pinecone MCP

The Pinecone MCP connects agents to Pinecone's managed vector database for retrieval and semantic search over your own data. A reliable, production-grade choice for RAG, with pricing that scales from a free tier to serverless usage-based plans.

Frequently Asked Questions

Tags

#vector-database#rag#semantic-search