The Model Context Protocol (MCP) is a standard that allows coding assistants and other AI tools to interact with platforms like Pinecone. The Pinecone Developer MCP Server enables you to connect these tools with Pinecone projects and documentation. Once connected, AI tools can search Pinecone documentation to answer questions accurately, help configure indexes based on your application's needs, generate code informed by your index configuration and Pinecone documentation and examples, and upsert and search for data in indexes to test queries and evaluate results within your dev environment. The MCP server serves as an integration layer between AI assistants and Pinecone’s platform, improving the developer experience when building and testing LLM-powered workflows. It is focused on developers working with Pinecone as part of their technology stack and is complementary to Pinecone's Assistant MCP, which provides AI assistants with knowledge from your knowledge base. Setup requires a Pinecone API key and Node.js. Configure the MCP server via .cursor/mcp.json in a project or via Claude Desktop or Gemini CLI extension. Limitations include support only for indexes with integrated inference. Tools available include search-docs, list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents.