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.