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sirmews/mcp-pinecone

by Various

Model Context Protocol server to allow for reading and writing from Pinecone. Rudimentary RAG

S

MCP

sirmews/mcp-pinecone

Added 1 June 2026

#claude #mcp #mcp-server #model-context-protocol #pinecone #rag

Overview

A Model Context Protocol server that connects LLM hosts to Pinecone vector databases for basic retrieval-augmented generation. It provides read and write operations against a Pinecone index via the standard MCP interface.

Best for

Best for
Developers who need a minimal MCP connector to add Pinecone-based memory or knowledge retrieval to an LLM client.

Use cases

  • Attach a Pinecone knowledge base to an MCP-compatible LLM client for context retrieval
  • Insert documents or embeddings into a Pinecone index from an LLM session
  • Query a Pinecone index for similar vectors during a chat or agent workflow

How to use

Install

npx -y @smithery/cli install mcp-pinecone --client claude

Tools exposed

  • semantic-search
  • read-document
  • list-documents
  • pinecone-stats
  • process-document

Tested with

Claude Desktop

Notes

A Model Context Protocol server that connects LLM hosts to Pinecone vector databases for basic retrieval-augmented generation. It provides read and write operations against a Pinecone index via the standard MCP interface.

149 stars on GitHub. Last updated 2025-01-31. Licensed MIT.

Use cases

  • Attach a Pinecone knowledge base to an MCP-compatible LLM client for context retrieval
  • Insert documents or embeddings into a Pinecone index from an LLM session
  • Query a Pinecone index for similar vectors during a chat or agent workflow

Pros

  • Adheres to the Model Context Protocol for easy integration with MCP-supporting tools
  • Lightweight Python server with a focused feature set
  • Straightforward read and write operations for basic RAG workflows

Cons

  • Limited to rudimentary RAG with no built-in chunking or embedding management
  • Relies on the user to manage Pinecone index configuration and API keys externally
  • Small community (149 stars) and single maintainer may affect long-term support

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Adheres to the Model Context Protocol for easy integration with MCP-supporting tools
  • Lightweight Python server with a focused feature set
  • Straightforward read and write operations for basic RAG workflows

Cons

  • Limited to rudimentary RAG with no built-in chunking or embedding management
  • Relies on the user to manage Pinecone index configuration and API keys externally
  • Small community (149 stars) and single maintainer may affect long-term support
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