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- Use case: Clean up outdated information or reset database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
ListToolsRequestSchemahandler - Implement tool logic in
CallToolRequestSchemahandler - 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