This MCP server implementation, developed by Bary Huang, provides integration with any OpenAPI-compliant service. Built with Python and leveraging libraries like FastAPI, FAISS, and sentence-transformers, it offers a flexible approach to connecting AI models with diverse APIs. The implementation focuses on dynamic tool generation based on OpenAPI specifications, enabling seamless interaction with a wide range of services without requiring custom code for each integration. It supports vector-based semantic search for efficient API operation discovery, making it particularly useful for applications that need to work with multiple or frequently changing APIs. By abstracting the complexities of API integration, this server allows for rapid development of AI-powered applications that can leverage external services and data sources through a standardized interface.
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Highlight AIGet API endpoint schemas that match your intent. Returns endpoint details including path, method, parameters, and response formats. Input Schema: { 'query': { 'type': 'string', 'description': 'Describe what you want to do with the API (e.g., Get user profile information, Create a new job posting)' } }
Essential for reliable execution with complex APIs where simplified implementations fail. Provides: Input Schema: { 'method': { 'type': 'string', 'description': 'HTTP method (GET, POST, PUT, DELETE, PATCH)', 'enum': ['GET', 'POST', 'PUT', 'DELETE', 'PATCH'] }, 'url': { 'type': 'string', 'description': 'Fully qualified API URL (e.g., https://api.example.com/users/123)' }, 'headers': { 'type': 'object', 'description': 'Request headers (optional)', 'additionalProperties': { 'type': 'string' } }, 'query_params': { 'type': 'object', 'description': 'Query parameters (optional)', 'additionalProperties': { 'type': 'string' } }, 'body': { 'type': 'object', 'description': 'Request body for POST, PUT, PATCH (optional)' } } Response Format: { 'status_code': 200, 'headers': { 'content-type': 'application/json', ... }, 'body': { // Response data } }
