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Complete MCP server exposing 15 Pinecone API operations to AI agents.
This workflow converts the Pinecone API into an MCP-compatible interface for AI agents.
• MCP Trigger : Serves as your server endpoint for AI agent requests
• HTTP Request Nodes : Handle API calls to https://controller.{environment}.pinecone.io
• AI Expressions : Automatically populate parameters via $fromAI() placeholders
• Native Integration : Returns responses directly to the AI agent
• GET /collections : Describe Collection
• POST /collections : Create collection
• DELETE /collections/{collectionName} : Delete Collection
• GET /collections/{collectionName} : Describe collection
• POST /describe_index_stats : Retrieve Index Stats
• GET /indexes : Configure Index
• POST /indexes : Create index
• DELETE /indexes/{indexName} : Delete Index
• GET /indexes/{indexName} : Describe index
• PATCH /indexes/{indexName} : Configure index
• POST /query : Execute Query
• POST /vectors/delete : Delete Vectors
• POST /vectors/fetch : Fetch Vectors
• POST /vectors/update : Update Vectors
• POST /vectors/upsert : Upsert Vectors
Parameter Handling : AI agents automatically provide values for:
• Path parameters and identifiers
• Query parameters and filters
• Request body data
• Headers and authentication
Response Format : Native Pinecone API responses with full data structure
Error Handling : Built-in n8n HTTP request error management
Connect this MCP server to any AI agent or workflow:
• Claude Desktop : Add MCP server URL to configuration
• Cursor : Add MCP server SSE URL to configuration
• Custom AI Apps : Use MCP URL as tool endpoint
• API Integration : Direct HTTP calls to MCP endpoints
• Zero Setup : No parameter mapping or configuration needed
• AI-Ready : Built-in $fromAI() expressions for all parameters
• Production Ready : Native n8n HTTP request handling and logging
• Extensible : Easily modify or add custom logic
🆓 Free for community use! Ready to deploy in under 2 minutes.