Pinecone Vector DB
This MCP server, developed by sirmews, enables AI assistants like Claude to interact with Pinecone vector databases for retrieval-augmented generation (RAG) applications. Built with Python, it provides tools for semantic search, document retrieval, and document upsert operations on Pinecone indexes. The implementation stands out by leveraging Pinecone's inference API for embedding generation, simplifying the architecture. By connecting AI capabilities with Pinecone's efficient vector search, this server allows AI systems to augment their knowledge with relevant information from large document collections. It is particularly useful for building AI applications that require real-time access to up-to-date information, such as question-answering systems, chatbots with domain-specific knowledge, or AI-powered research assistants.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/sirmews/mcp-pinecone
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Pinecone Vector DB from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.