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