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MCP Entry

Pinecone MCP

Enables AI agents to inspect Pinecone vector database indexes, query vectors, manage collections, and debug retrieval behavior. Useful for teams using vector search in RAG applications who want to understand index statistics and optimize similarity search performance.

Category Database
Install npm
Runtime Pinecone
vectordatabaserag

Use cases

  • ML engineer inspects index statistics to diagnose poor retrieval quality
  • Developer queries vectors to verify embeddings were upserted correctly
  • DBA reviews collection configurations for archiving old vector data
  • QA agent validates RAG pipeline by fetching similar results manually
  • Architect evaluates index scaling based on dimension and pod usage stats

Key features

  • Claude Desktop
  • Cursor
  • VS Code

Frequently Asked Questions

What Pinecone tier is required for MCP access?
MCP is available on Starter tier and above. Serverless indexes have limited metadata access. Check Pinecone documentation for your specific plan's capabilities.
Can agents query vectors for similarity search?
Yes, agents can run query operations to find similar vectors using cosine, dot product, or Euclidean distance metrics depending on your index configuration.
Does it support metadata filtering?
Yes, agents can use metadata filters in queries if your index is configured with sparse-raw indexes or supports structured metadata filtering.

Related

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3 Indexed items