mcp-pinecone

A Model Context Protocol (MCP) server for interacting with Pinecone, enabling vector search and document management capabilities over Pinecone indexes.

• Source Code: GitHub - sirmews/mcp-pinecone
• Category: database-messaging-mcp-servers
• Tags: mcp, pinecone, vector-database, semantic-search, ai-integration

Features

• Read and Write to Pinecone Index: Provides an MCP server interface to interact with Pinecone indexes for both reading and writing data.
• Rudimentary RAG (Retrieval-Augmented Generation): Supports basic RAG workflows.
• Semantic Search: Allows searching for records in a Pinecone index using semantic embeddings.
• Read Document: Fetches a document from a Pinecone index.
• List Documents: Lists all documents available in the Pinecone index.
• Pinecone Stats: Retrieves index statistics, such as the number of records, dimensions, and namespaces.
• Process Document: Processes a document into token-based chunks, generates embeddings, and upserts them into the Pinecone index. Embeddings are generated via Pinecone's inference API.
• Request Handlers: Implements MCP handlers such as list_resources, read_resource, list_tools, call_tool, get_prompt, and list_prompts.
• Development Tools: Includes a Dockerfile, Makefile, and supports installation via PyPI or Smithery CLI.
• Debugging Support: Compatible with MCP Inspector for debugging MCP servers.

Installation

• PyPI: uv pip install mcp-pinecone
• Smithery CLI: npx -y @smithery/cli install mcp-pinecone --client claude
• Docker: Dockerfile included in the repository for containerized setup.

Configuration

• Requires a Pinecone account, API key, and index name for operation.
• Supports configuration for both development (unpublished) and published server setups.

License

MIT License

Pricing

No pricing information is provided. The project is open source under MIT License.