Pinecone MCP Server

A Model Context Protocol server for Pinecone vector database operations.

This MCP server provides programmatic access to Pinecone vector database operations, enabling AI assistants to perform semantic search, manage vectors, and interact with your knowledge base through standardized MCP tools.

Features

Tools

🔍 `query_vectors`

Perform semantic search on your Pinecone database

Input: Text query, optional top_k and include_metadata parameters
Output: JSON response with matching vectors and similarity scores
Use case: Find relevant documents based on natural language queries

➕ `upsert_vectors`

Add new documents to your vector database

Input: Array of texts, optional metadata and IDs
Output: Confirmation of successful vector insertion
Use case: Index new documents or update existing knowledge base

🗑️ `delete_vectors`

Remove vectors from your database

Input: Array of vector IDs or delete_all flag
Output: Confirmation of deletion operation
Use case: Clean up outdated information or reset database

📊 `get_index_stats`

Monitor your Pinecone database

Input: None
Output: Index statistics including vector count and configuration
Use case: Track database usage and performance

Quick Start

Prerequisites

Node.js 18+
Pinecone account and API key
OpenAI API key (for embeddings)

Installation

Clone and install dependencies:

git clone <your-repo-url>
cd pinecone-mcp-server
npm install

Build the server:

npm run build

Configure environment variables:

export PINECONE_API_KEY="your_pinecone_key"
export OPENAI_API_KEY="your_openai_key"
export PINECONE_INDEX_NAME="your_index_name"  # optional, defaults to "ad-assessor-docs"

Run the server:

node build/index.js

MCP Configuration

For Claude Desktop

Add to your MCP configuration file:

MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "pinecone": {
      "command": "node",
      "args": ["/path/to/pinecone-mcp-server/build/index.js"],
      "env": {
        "PINECONE_API_KEY": "your_key_here",
        "OPENAI_API_KEY": "your_key_here",
        "PINECONE_INDEX_NAME": "your_index_name"
      }
    }
  }
}

For Cline (VSCode)

Add to: %APPDATA%/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

{
  "mcpServers": {
    "pinecone": {
      "command": "node",
      "args": ["C:\\path\\to\\pinecone-mcp-server\\build\\index.js"],
      "env": {
        "PINECONE_API_KEY": "your_key_here",
        "OPENAI_API_KEY": "your_key_here",
        "PINECONE_INDEX_NAME": "your_index_name"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Docker Deployment

Build Docker Image

docker build -t pinecone-mcp .

Run Container

docker run -e PINECONE_API_KEY=your_key \
           -e OPENAI_API_KEY=your_key \
           -e PINECONE_INDEX_NAME=your_index \
           -p 3000:3000 \
           pinecone-mcp

Docker Compose

version: '3.8'
services:
  pinecone-mcp:
    build: .
    environment:
      - PINECONE_API_KEY=your_key
      - OPENAI_API_KEY=your_key
      - PINECONE_INDEX_NAME=your_index
    ports:
      - "3000:3000"

Development

Project Structure

pinecone-server/
├── src/
│   └── index.ts          # Main MCP server implementation
├── build/
│   └── index.js          # Compiled JavaScript
├── Dockerfile            # Docker configuration
├── package.json          # Dependencies and scripts
├── tsconfig.json         # TypeScript configuration
└── README.md            # This file

Development Commands

# Install dependencies
npm install

# Build for production
npm run build

# Development with auto-rebuild
npm run watch

# Debug with MCP Inspector
npm run inspector

Adding New Tools

Define tool schema in ListToolsRequestSchema handler
Implement tool logic in CallToolRequestSchema handler
Update this README with new tool documentation

API Keys Setup

Pinecone

Sign up at pinecone.io
Create a new project and index
Copy your API key from the dashboard

OpenAI

Sign up at platform.openai.com
Navigate to API Keys section
Create a new secret key

Troubleshooting

Common Issues

"Cannot find module" errors:

Ensure all dependencies are installed: npm install
Check that the build completed successfully: npm run build

Pinecone connection issues:

Verify API key is correct and has proper permissions
Check that your index exists and is accessible
Ensure your Pinecone environment/region is correct

OpenAI API errors:

Confirm API key is valid and has credits
Check rate limits and usage quotas
Verify the model name is correct (text-embedding-ada-002)

Debugging

Use the MCP Inspector for debugging:

npm run inspector

This provides a web interface to test your MCP server interactively.

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

License

MIT License - see LICENSE file for details

Support

For issues and questions:

Open an issue on GitHub
Check the MCP documentation: https://modelcontextprotocol.io
Review Pinecone documentation: https://docs.pinecone.io

Pinecone MCP Server