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HTTP transport enables efficient streaming of large responses and supports modern MCP client features, while SSE transport maintains compatibility with clients that only support the legacy protocol.","intents":["I want to support both new HTTP-based MCP clients and legacy SSE-based clients with a single server","I need efficient streaming of large tool responses without buffering entire payloads","I want to choose the optimal transport for my deployment environment (cloud vs edge)"],"best_for":["organizations with mixed MCP client versions requiring backward compatibility","applications serving large streaming responses (file uploads, data exports)","deployments where transport choice affects performance (HTTP for cloud, SSE for edge)"],"limitations":["SSE transport does not support streaming responses — entire response must be buffered before sending","HTTP transport requires MCP client support for HTTP protocol (not all legacy clients support this)","Switching transports requires server restart — cannot dynamically change transport per client"],"requires":["Python 3.9+","FastAPI 0.68.0+","MCP client compatible with at least one of HTTP or SSE protocols"],"input_types":["MCP protocol messages","Client transport preference (HTTP or SSE)"],"output_types":["MCP protocol responses","Streamed or buffered tool results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-tadata-org-fastapi_mcp__cap_4","uri":"capability://safety.moderation.authentication.and.authorization.configuration.with.oauth.2.1.and.jwt.support","name":"authentication and authorization configuration with oauth 2.1 and jwt support","description":"Provides declarative authentication configuration (AuthConfig type) that integrates with FastAPI's security schemes, supporting OAuth 2.1, JWT, and custom authentication handlers. 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Authentication is configured at the MCP server level and automatically applied to all exposed endpoints, with support for custom auth validators and token forwarding.","intents":["I want to expose only authenticated endpoints via MCP, with auth credentials forwarded from MCP clients","I need to use my existing FastAPI OAuth 2.1 or JWT authentication with MCP clients","I want to implement custom authorization logic that applies to MCP tool calls"],"best_for":["applications with sensitive endpoints requiring authentication before MCP exposure","teams using OAuth 2.1 or JWT in FastAPI who want to extend auth to MCP clients","systems requiring per-endpoint authorization checks for MCP tool calls"],"limitations":["Authentication configuration is global to the MCP server — cannot have different auth schemes for different endpoints","Custom auth validators must be compatible with FastAPI's Depends() pattern","Token refresh logic must be implemented by the client — server does not auto-refresh expired tokens"],"requires":["FastAPI application with security schemes configured (OAuth 2.1, JWT, etc.)","Python 3.9+","MCP client capable of sending authentication headers or tokens"],"input_types":["Authentication configuration (OAuth 2.1 settings, JWT secrets)","MCP client credentials (tokens, API keys)"],"output_types":["Authenticated request context","User identity for FastAPI Depends() injection"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-tadata-org-fastapi_mcp__cap_5","uri":"capability://safety.moderation.endpoint.filtering.and.selective.exposure","name":"endpoint filtering and selective exposure","description":"Allows selective exposure of FastAPI endpoints as MCP tools through filtering configuration, enabling developers to exclude sensitive endpoints, internal utilities, or endpoints not suitable for LLM invocation. Filtering can be applied by endpoint path, method, tags, or custom predicates, giving fine-grained control over which endpoints become MCP tools. This prevents accidental exposure of administrative endpoints or endpoints with side effects unsuitable for autonomous LLM execution.","intents":["I want to expose only specific FastAPI endpoints as MCP tools, hiding internal or sensitive endpoints","I need to exclude endpoints with dangerous side effects from LLM access","I want to use endpoint tags or naming conventions to control which endpoints become MCP tools"],"best_for":["applications with mixed endpoint purposes (public API, admin, internal utilities)","teams concerned about LLM agents invoking dangerous or sensitive endpoints","systems requiring fine-grained control over which endpoints are MCP-accessible"],"limitations":["Filtering is applied at server initialization — cannot dynamically change which endpoints are exposed without restart","Custom filter predicates must be defined in Python code — no declarative filtering syntax","Filtering is based on OpenAPI schema metadata — endpoints without proper documentation may be hard to filter"],"requires":["FastAPI application with properly documented endpoints (tags, descriptions)","Python 3.9+","Knowledge of endpoint paths, methods, or tags for filter configuration"],"input_types":["Filter configuration (paths, methods, tags, predicates)","FastAPI endpoint definitions"],"output_types":["Filtered list of MCP tools","Excluded endpoints (for audit)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-tadata-org-fastapi_mcp__cap_6","uri":"capability://data.processing.analysis.response.schema.customization.and.output.formatting","name":"response schema customization and output formatting","description":"Allows customization of how FastAPI endpoint responses are formatted and presented to MCP clients through response schema configuration. Developers can define custom response schemas, transform endpoint outputs, or customize error formatting to match MCP expectations. This enables adaptation of FastAPI responses (which may be optimized for HTTP clients) to MCP tool output requirements, including handling of streaming responses, large payloads, and error conditions.","intents":["I want to transform FastAPI endpoint responses into formats optimized for LLM consumption","I need to customize error messages and status codes for MCP clients","I want to handle streaming or large responses efficiently in MCP"],"best_for":["applications where FastAPI response formats don't match MCP tool output expectations","systems with complex response types requiring transformation for LLM consumption","teams needing custom error handling for MCP clients"],"limitations":["Response customization is defined per-endpoint — global transformations require custom middleware","Streaming responses require HTTP transport — SSE transport buffers entire response","Large response payloads may exceed MCP message size limits — no automatic pagination"],"requires":["FastAPI application with response models defined","Python 3.9+","Understanding of MCP response format requirements"],"input_types":["FastAPI endpoint response","Response schema configuration"],"output_types":["Formatted MCP tool output","Transformed response data"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-tadata-org-fastapi_mcp__cap_7","uri":"capability://automation.workflow.same.app.and.separate.app.deployment.patterns","name":"same-app and separate-app deployment patterns","description":"Supports two deployment architectures: same-app deployment where the MCP server and FastAPI application run in the same Python process (using ASGI transport for zero-copy execution), and separate-app deployment where the MCP server runs in a separate process and communicates with FastAPI via HTTP. 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