Pinecone MCP Server

What is this?

The Pinecone MCP Server is a connector that bridges LLM agents with the Pinecone vector database using the Model Context Protocol (MCP). It provides a drop-in solution to perform embedding-driven operations like semantic search, similarity retrieval, and RAG pipelines without dealing with low-level API details. Developers can install it as the @modelcontextprotocol/server-pinecone package and instantly expose upsert, query, fetch, and delete endpoints that any MCP-capable client can call.

Under the hood, it runs as a Node.js/TypeScript service, wrapping Pinecone’s official client to handle batching, retries, and namespace isolation. This means you get enterprise-grade performance, security, and scalability out of the box. Whether you’re indexing customer transcripts for a chatbot or powering a RAG experience over internal docs, the Pinecone MCP Server makes it easy to swap vector backends without rewriting your LLM logic.

Quick Start

Install the server using npm:

npm install @modelcontextprotocol/server-pinecone

Then add it to your MCP client configuration:

{
  "mcpServers": {
    "pinecone": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-pinecone"],
      "env": {
        "API_KEY": "your-api-key-here"
      }
    }
  }
}

Key Features

Feature 1: MCP-Compliant Connector that exposes standardized upsert, search, fetch, and delete operations via REST or gRPC.

Feature 2: Secure API Key & Namespace Isolation leveraging Pinecone’s authentication and multi-tenant namespaces.

Feature 3: High Throughput & Low Latency real-time vector search at scale with built-in batching and retry logic.

Example Usage

Use the built-in search tool to retrieve the top 5 semantically related passages before each LLM call.

// Example code
const result = await client.callTool({
  name: "PineconeSearchTool",
  arguments: {
    query: { text: "How do I reset my device?" },
    topK: 5
  }
});

This code invokes the MCP search endpoint and returns the 5 most similar vectors, including their metadata and scores.

Configuration

The server accepts the following environment variables:

API_KEY – Your Pinecone API key for authenticating requests.

NAMESPACE (optional) – Default Pinecone namespace to use for all operations.

Available Tools/Resources

PineconeSearchTool: Performs semantic search queries via the MCP endpoint.

PineconeUpsertTool: Handles batch upserts of embeddings and metadata to Pinecone.

Who Should Use This?

This server is perfect for:

Use case 1: AI developers building RAG chatbots over proprietary documentation.

Use case 2: Teams needing scalable, low-latency semantic search in production environments.

Use case 3: Organizations requiring multi-tenant vector storage with secure namespace isolation.

Conclusion

The Pinecone MCP Server makes it easy to integrate Pinecone’s vector database into your LLM workflows using a universal protocol. Enjoy seamless RAG, semantic search, and scalable vector storage with minimal setup. Give it a try and let us know what you build!

Check out the GitHub repository for more information and to contribute.