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Uses the convert_openapi_to_mcp_tools() function to parse OpenAPI 3.0 specifications, extracting parameter definitions, request/response schemas, and documentation, then maps them to MCP tool format with preserved validation rules and type information. This enables LLMs to understand and invoke FastAPI endpoints as native tools.","intents":["I want to expose my existing FastAPI endpoints to Claude/Cursor without manually writing tool definitions","I need to keep my OpenAPI documentation in sync with MCP tool schemas automatically","I want to preserve Pydantic validators and type hints when converting endpoints to MCP tools"],"best_for":["teams with existing FastAPI applications seeking LLM integration","developers building AI agents that need to invoke REST APIs as tools","organizations wanting zero-copy integration between FastAPI and MCP clients"],"limitations":["Requires FastAPI application to have properly documented OpenAPI schema (missing or malformed schemas will produce incomplete tool definitions)","Complex nested Pydantic models may produce verbose MCP schemas that exceed context limits","Does not support OpenAPI 2.0 (Swagger) — only OpenAPI 3.0+"],"requires":["FastAPI application instance with OpenAPI schema generation enabled","Python 3.9+","Pydantic models for request/response validation"],"input_types":["FastAPI application object","OpenAPI schema (JSON/YAML)"],"output_types":["MCP tool definitions (JSON schema format)","Tool metadata with descriptions and parameter constraints"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_1","uri":"capability://tool.use.integration.asgi.based.zero.copy.tool.execution.with.fastapi.delegation","name":"asgi-based zero-copy tool execution with fastapi delegation","description":"Translates MCP tool calls directly to FastAPI endpoint invocations using ASGI transport, bypassing HTTP overhead by communicating directly with the FastAPI application instance. 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This approach preserves middleware execution, dependency injection, and authentication context.","intents":["I want LLM tool calls to execute against my FastAPI endpoints with minimal latency","I need to preserve FastAPI middleware, dependency injection, and request context during tool execution","I want to avoid HTTP serialization overhead when invoking endpoints from MCP clients"],"best_for":["high-performance AI agent systems requiring sub-100ms tool execution latency","same-process deployments where FastAPI app and MCP server run in the same Python runtime","applications with complex FastAPI middleware or dependency injection that must be preserved"],"limitations":["Only works with same-process deployments — cannot invoke remote FastAPI instances (use HTTP transport for separate-app deployments)","Requires FastAPI application to be instantiated in the same Python process","ASGI transport does not support streaming responses in legacy SSE transport mode"],"requires":["FastAPI application instance accessible in Python runtime","ASGI-compatible transport layer","Python 3.9+"],"input_types":["MCP tool call with parameters","FastAPI ASGI application instance"],"output_types":["HTTP response body (JSON, text, or binary)","Response headers and status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_10","uri":"capability://tool.use.integration.error.handling.and.response.status.code.propagation","name":"error handling and response status code propagation","description":"Propagates HTTP error responses and status codes from FastAPI endpoints back to MCP clients, preserving error semantics and enabling LLMs to understand and handle failures appropriately. 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This enables LLMs to distinguish between different error types (validation errors, authentication failures, server errors) and respond accordingly.","intents":["I want my AI agent to understand when a tool call fails and why (validation error vs server error)","I need to propagate HTTP error responses from FastAPI to MCP clients","I want LLMs to handle different error types appropriately in their reasoning"],"best_for":["applications where LLMs need to understand tool failure reasons","systems with complex error handling that must be preserved across MCP","AI agents requiring error recovery and retry logic"],"limitations":["Error propagation is limited to HTTP status codes and response bodies — no access to FastAPI exception details","Custom exception handlers in FastAPI are not directly accessible; only the resulting HTTP response is propagated","Error messages are limited to what FastAPI includes in the response body","No automatic error recovery or retry logic — LLMs must implement their own"],"requires":["FastAPI application with proper error handling","HTTP status codes and error responses properly formatted","Python 3.9+"],"input_types":["HTTP error responses from FastAPI endpoints"],"output_types":["MCP error responses with status codes and error messages"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_11","uri":"capability://automation.workflow.mcp.server.lifecycle.management.and.transport.mounting","name":"mcp server lifecycle management and transport mounting","description":"Manages the complete MCP server lifecycle including initialization, transport mounting, and shutdown. 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Lifecycle management includes resource cleanup, session termination, and proper transport shutdown.","intents":["I want to initialize and start an MCP server that exposes my FastAPI endpoints","I need to properly shut down the MCP server and clean up resources","I want to mount the MCP server on my FastAPI application or run it separately"],"best_for":["developers building MCP servers from FastAPI applications","teams needing proper server lifecycle management and resource cleanup","applications requiring graceful shutdown and session termination"],"limitations":["Lifecycle management is synchronous — no async initialization hooks","No built-in health checks or monitoring — must be implemented separately","Server restart requires full re-initialization; no hot-reload support","Resource cleanup is basic — no support for complex shutdown sequences"],"requires":["FastAPI application instance","Transport configuration","Python 3.9+"],"input_types":["FastAPI application instance","Transport configuration"],"output_types":["Running MCP server instance"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_12","uri":"capability://tool.use.integration.native.fastapi.dependency.injection.and.middleware.preservation","name":"native fastapi dependency injection and middleware preservation","description":"Preserves FastAPI's dependency injection system and middleware execution when invoking endpoints through MCP tools. The ASGI-based tool execution layer reconstructs the full FastAPI request context, enabling dependencies (database connections, authentication, logging) and middleware (CORS, compression, custom handlers) to execute normally. This ensures that MCP-invoked endpoints behave identically to HTTP-invoked endpoints, with all side effects and validations intact.","intents":["I want my FastAPI dependencies (database sessions, auth) to work when endpoints are called via MCP","I need middleware (logging, CORS, compression) to execute for MCP tool calls","I want MCP-invoked endpoints to behave identically to HTTP-invoked endpoints"],"best_for":["applications with complex FastAPI dependency graphs","systems with middleware that must execute for all endpoint invocations","teams wanting to avoid duplicating dependency logic for MCP"],"limitations":["Dependency injection only works with same-app deployment (ASGI transport) — separate-app deployments cannot access FastAPI dependencies","Middleware execution adds latency (~10-50ms per request) compared to direct endpoint invocation","Request context reconstruction may fail for complex dependencies with side effects","Async dependencies require proper async context management"],"requires":["Same-app deployment (ASGI transport)","FastAPI application with dependencies configured","Python 3.9+"],"input_types":["MCP tool call"],"output_types":["Endpoint response with all dependencies and middleware executed"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_2","uri":"capability://tool.use.integration.stateful.http.session.management.for.multi.turn.mcp.interactions","name":"stateful http session management for multi-turn mcp interactions","description":"Manages persistent HTTP client sessions across multiple MCP tool calls using the FastApiHttpSessionManager class, enabling stateful interactions with FastAPI endpoints. Maintains session state (cookies, headers, authentication tokens) across tool invocations, allowing LLMs to authenticate once and execute multiple authenticated requests without re-authentication. Sessions are keyed by client identifier and support concurrent multi-turn conversations.","intents":["I want my AI agent to authenticate once and maintain that session across multiple tool calls","I need to preserve cookies and session state across multiple MCP tool invocations","I want to support multi-turn conversations where authentication state persists between turns"],"best_for":["AI agents executing multi-step workflows requiring persistent authentication","applications with stateful FastAPI endpoints that rely on session cookies","separate-app deployments using HTTP transport where session management is critical"],"limitations":["Sessions are in-memory by default — no persistence across server restarts","Session timeout and cleanup must be manually configured; no automatic garbage collection","Concurrent sessions for the same client may cause race conditions if not properly synchronized","Does not support distributed session storage (e.g., Redis) out-of-the-box"],"requires":["HTTP transport enabled (not ASGI transport)","Client identifier provided in MCP requests","Python 3.9+"],"input_types":["MCP tool call with client context","HTTP headers and cookies from previous requests"],"output_types":["Updated session state (cookies, headers)","Authenticated HTTP responses"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_3","uri":"capability://tool.use.integration.flexible.endpoint.filtering.and.selective.tool.exposure","name":"flexible endpoint filtering and selective tool exposure","description":"Filters FastAPI endpoints before converting them to MCP tools using configurable inclusion/exclusion patterns, path prefixes, and tag-based filtering. Allows developers to selectively expose only specific endpoints as MCP tools while keeping internal or sensitive endpoints hidden. Filtering is applied during schema conversion, preventing unwanted endpoints from appearing in the MCP tool registry.","intents":["I want to expose only specific FastAPI endpoints as MCP tools, hiding internal or admin endpoints","I need to filter endpoints by path prefix or tag to control what LLMs can access","I want to prevent sensitive endpoints from being exposed to MCP clients"],"best_for":["applications with mixed public/internal endpoints requiring selective exposure","security-conscious teams wanting fine-grained control over LLM access","multi-tenant systems where different clients should see different endpoint subsets"],"limitations":["Filtering is static and applied at initialization time — cannot dynamically change exposed endpoints at runtime","No role-based filtering — all authenticated clients see the same filtered endpoint set","Filtering rules are not enforced at the FastAPI layer, only at MCP tool generation"],"requires":["FastAPI application instance","Filter configuration (path patterns, tags, or inclusion/exclusion lists)","Python 3.9+"],"input_types":["FastAPI application with OpenAPI schema","Filter configuration (regex patterns, tags, or path prefixes)"],"output_types":["Filtered MCP tool definitions","Subset of endpoints exposed as MCP tools"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_4","uri":"capability://tool.use.integration.authentication.forwarding.with.oauth.2.1.jwt.and.custom.auth.support","name":"authentication forwarding with oauth 2.1, jwt, and custom auth support","description":"Forwards authentication credentials from MCP clients to FastAPI endpoints using configurable authentication strategies including OAuth 2.1, JWT tokens, API keys, and custom authentication handlers. The AuthConfig class encapsulates authentication metadata, and the HTTPRequestInfo type carries request context (headers, cookies) through the tool execution pipeline. Supports both bearer token forwarding and header-based authentication, preserving the original FastAPI authentication requirements.","intents":["I want MCP clients to authenticate and have that authentication forwarded to my FastAPI endpoints","I need to support JWT token forwarding from MCP clients to protected FastAPI endpoints","I want to use my existing FastAPI OAuth 2.1 or API key authentication with MCP clients"],"best_for":["applications with existing FastAPI authentication that need to extend to MCP clients","teams using OAuth 2.1 or JWT for API security","systems requiring per-request authentication forwarding to preserve audit trails"],"limitations":["Authentication is forwarded as-is from MCP client to FastAPI — no token refresh or re-authentication logic","Custom authentication handlers must be implemented by the developer; no built-in support for complex auth flows","Does not support multi-factor authentication or challenge-response flows","Bearer token forwarding assumes standard Authorization header format"],"requires":["FastAPI application with authentication configured","AuthConfig object with authentication strategy specified","MCP client capable of providing authentication credentials","Python 3.9+"],"input_types":["MCP tool call with authentication context","Bearer tokens, API keys, or custom auth headers"],"output_types":["Authenticated HTTP requests to FastAPI endpoints","Responses from authenticated endpoints"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_5","uri":"capability://tool.use.integration.dual.transport.support.http.and.sse.with.modern.streaming.capabilities","name":"dual transport support (http and sse) with modern streaming capabilities","description":"Supports both modern HTTP transport (recommended for streaming responses) and legacy Server-Sent Events (SSE) transport for backward compatibility with older MCP clients. HTTP transport enables streaming tool responses and supports concurrent requests, while SSE transport provides fallback compatibility for clients that don't support HTTP. Transport selection is configurable at initialization, and both transports share the same tool execution pipeline.","intents":["I want to support both modern MCP clients (using HTTP) and legacy clients (using SSE)","I need streaming responses for long-running tool executions","I want to choose between HTTP and SSE transport based on client requirements"],"best_for":["applications supporting multiple MCP client versions","systems requiring streaming responses for long-running operations","deployments needing backward compatibility with legacy MCP clients"],"limitations":["SSE transport does not support streaming responses — responses are buffered and sent as complete messages","HTTP transport requires HTTP/1.1 or HTTP/2 support from the client","Transport selection is static at initialization — cannot switch transports per-request","SSE transport has higher latency due to message buffering"],"requires":["FastAPI application instance","Transport configuration (HTTP or SSE)","Python 3.9+"],"input_types":["MCP protocol messages","Tool call requests"],"output_types":["Streamed or buffered responses","MCP protocol responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_6","uri":"capability://automation.workflow.same.app.and.separate.app.deployment.patterns.with.flexible.architecture","name":"same-app and separate-app deployment patterns with flexible architecture","description":"Supports two deployment architectures: same-app deployment (MCP server and FastAPI app in the same process using ASGI transport for zero-copy execution) and separate-app deployment (MCP server in separate process using HTTP transport to invoke remote FastAPI endpoints). Deployment pattern is selected at initialization and determines whether ASGI or HTTP transport is used. This flexibility enables both high-performance co-located deployments and distributed architectures.","intents":["I want to run my MCP server and FastAPI app in the same process for maximum performance","I need to deploy my MCP server separately from my FastAPI app for independent scaling","I want to choose between same-app and separate-app deployment based on my infrastructure"],"best_for":["monolithic applications where co-location is feasible","microservices architectures requiring independent MCP server deployment","teams wanting to optimize for either latency (same-app) or scalability (separate-app)"],"limitations":["Same-app deployment requires FastAPI app to be instantiated in the same Python process — not suitable for remote FastAPI instances","Separate-app deployment adds HTTP serialization overhead (~50-200ms per request) compared to same-app","Deployment pattern is static at initialization — cannot switch between patterns at runtime","Same-app deployment couples MCP server lifecycle to FastAPI app lifecycle"],"requires":["FastAPI application instance (same-app) or HTTP endpoint (separate-app)","Deployment configuration specifying pattern","Python 3.9+"],"input_types":["FastAPI application instance or HTTP endpoint URL"],"output_types":["MCP server instance configured for selected deployment pattern"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_7","uri":"capability://tool.use.integration.response.schema.customization.and.output.type.mapping","name":"response schema customization and output type mapping","description":"Allows customization of how FastAPI endpoint responses are mapped to MCP tool output schemas through response schema configuration. Developers can define custom output schemas, specify which response fields to expose, and control how complex response types are serialized for MCP clients. This enables fine-grained control over what information is returned to LLMs while hiding internal response details.","intents":["I want to customize which fields from my FastAPI responses are exposed to MCP clients","I need to transform complex response objects into simpler schemas for LLM consumption","I want to hide internal response details while exposing only relevant data to AI agents"],"best_for":["applications with complex response schemas that need simplification for LLMs","teams wanting to decouple internal API responses from LLM-facing schemas","systems requiring field-level control over what data is exposed to AI agents"],"limitations":["Response schema customization is static and applied at initialization — cannot dynamically change schemas per-request","Complex transformations require custom schema definitions; no automatic schema simplification","Does not support conditional field inclusion based on authentication or request context","Schema customization is not enforced at the FastAPI layer, only at MCP response generation"],"requires":["FastAPI application with response models","Response schema configuration (custom Pydantic models or schema definitions)","Python 3.9+"],"input_types":["FastAPI endpoint response objects"],"output_types":["Customized MCP tool output schemas","Transformed response data"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_8","uri":"capability://tool.use.integration.dynamic.route.registration.and.runtime.endpoint.discovery","name":"dynamic route registration and runtime endpoint discovery","description":"Supports dynamic route registration where FastAPI endpoints can be added or modified at runtime, and the MCP server can discover and expose these new endpoints without restart. The library introspects the FastAPI OpenAPI schema at initialization and can be reconfigured to pick up new routes. This enables applications with dynamic endpoint generation (e.g., plugin systems, multi-tenant APIs) to automatically expose new endpoints as MCP tools.","intents":["I want my MCP server to automatically discover new FastAPI endpoints added at runtime","I need to support plugin systems where endpoints are dynamically registered","I want to expose multi-tenant endpoints that are generated dynamically"],"best_for":["plugin-based architectures with dynamic endpoint registration","multi-tenant systems generating endpoints per tenant","applications with evolving API surfaces that need automatic MCP tool updates"],"limitations":["Dynamic route discovery requires manual re-initialization of the MCP server — no automatic hot-reload","New routes are only discovered if they appear in the FastAPI OpenAPI schema","Removed routes are not automatically cleaned up from the MCP tool registry","Dynamic route registration adds complexity to deployment and testing"],"requires":["FastAPI application with dynamic route registration","Mechanism to trigger MCP server re-initialization when routes change","Python 3.9+"],"input_types":["FastAPI application with dynamically registered endpoints"],"output_types":["Updated MCP tool registry with new endpoints"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tadata-org--fastapi_mcp__cap_9","uri":"capability://tool.use.integration.http.client.configuration.and.request.customization","name":"http client configuration and request customization","description":"Provides configurable HTTP client settings for separate-app deployments, including custom headers, timeout configuration, SSL/TLS settings, and proxy support. 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