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• Use case: Clean up outdated information or reset database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

1. Clone and install dependencies:

2. Build the server:

3. Configure environment variables:

4. Run the server:

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

For Cline (VSCode)

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

Docker Deployment

Build Docker Image

Run Container

Docker Compose

Development

Project Structure

Development Commands

Adding New Tools

1. Define tool schema in ListToolsRequestSchema handler

2. Implement tool logic in CallToolRequestSchema handler

3. Update this README with new tool documentation

API Keys Setup

Pinecone

1. Sign up at pinecone.io

2. Create a new project and index

3. Copy your API key from the dashboard

OpenAI

1. Sign up at platform.openai.com

2. Navigate to API Keys section

3. 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:

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

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests if applicable

5. 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