{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-prefecthq-fastmcp","slug":"mcp-prefecthq-fastmcp","name":"fastmcp","type":"mcp","url":"https://github.com/PrefectHQ/fastmcp","page_url":"https://unfragile.ai/mcp-prefecthq-fastmcp","categories":["mcp-servers"],"tags":["agents","fastmcp","llms","mcp","mcp-clients","mcp-servers","mcp-tools","model-context-protocol","python"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-prefecthq-fastmcp__cap_0","uri":"capability://tool.use.integration.decorator.based.mcp.server.definition.with.automatic.schema.generation","name":"decorator-based mcp server definition with automatic schema generation","description":"FastMCP provides a Python decorator-based interface (@mcp.tool, @mcp.resource, @mcp.prompt) that automatically generates JSON-RPC schemas and MCP protocol compliance from function signatures and docstrings. The framework introspects Python type hints and Pydantic models to produce OpenAPI-compatible schemas without manual schema definition, eliminating boilerplate while maintaining full protocol compliance.","intents":["I want to expose Python functions as MCP tools without writing JSON schemas manually","I need to build an MCP server quickly without understanding the low-level protocol details","I want type safety and automatic validation for tool parameters"],"best_for":["Python developers building LLM-connected tools and services","Teams migrating from REST APIs to MCP without rewriting business logic","Rapid prototyping of agent-accessible capabilities"],"limitations":["Decorator-based approach requires functions to be defined at module load time; dynamic tool registration has limited support","Schema generation relies on type hints; untyped Python code requires manual schema specification","Pydantic v2 required for advanced validation; v1 compatibility limited"],"requires":["Python 3.9+","Pydantic v2.0+","FastMCP package installed via pip or uv"],"input_types":["Python function signatures with type hints","Pydantic BaseModel classes","Docstrings with parameter descriptions"],"output_types":["JSON-RPC 2.0 compatible tool definitions","OpenAPI 3.0 schemas","MCP ToolDefinition protocol messages"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_1","uri":"capability://tool.use.integration.transport.agnostic.client.with.pluggable.transport.backends","name":"transport-agnostic client with pluggable transport backends","description":"FastMCP's Client class abstracts the underlying transport layer through a provider pattern, supporting stdio, HTTP, SSE, and WebSocket transports without changing client code. The transport layer is decoupled from client logic via the Transport interface, allowing runtime selection of communication mechanism based on deployment context (local subprocess, remote server, cloud function).","intents":["I want to connect to MCP servers via different transports (stdio, HTTP, WebSocket) with a single client API","I need to switch between local and remote MCP servers without rewriting client code","I want to implement custom transport mechanisms for specialized deployment scenarios"],"best_for":["LLM application developers building multi-server agent architectures","Teams deploying MCP servers across heterogeneous infrastructure (local, cloud, edge)","Framework builders extending MCP with custom transport protocols"],"limitations":["Transport selection must be made at client initialization; runtime transport switching requires client recreation","Custom transport implementations require implementing the Transport interface; no automatic protocol negotiation","HTTP transport lacks built-in connection pooling; high-frequency calls may create connection overhead"],"requires":["Python 3.9+","FastMCP client module","Network connectivity for remote transports (HTTP, WebSocket)","Subprocess capability for stdio transport"],"input_types":["Transport configuration (URL, subprocess command, credentials)","MCP protocol messages (tool calls, resource requests)","Authentication tokens or credentials"],"output_types":["Tool execution results","Resource content","Prompt templates","Streaming responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_10","uri":"capability://safety.moderation.authentication.and.authorization.framework.with.pluggable.auth.backends","name":"authentication and authorization framework with pluggable auth backends","description":"FastMCP provides an authentication framework that supports multiple auth backends (API keys, OAuth2, JWT, custom) and integrates with the context system for request-scoped auth state. Authentication is decoupled from authorization through a pluggable auth provider interface, allowing teams to implement custom auth logic (LDAP, SAML, custom databases) without modifying the server. Auth state is accessible to tools via the context system.","intents":["I want to secure MCP server access with API keys, OAuth2, or custom authentication","I need to enforce per-user or per-role authorization on tool access","I want to integrate with existing identity systems (LDAP, SAML, custom databases)"],"best_for":["Multi-tenant MCP servers requiring strong isolation and per-user access control","Enterprise deployments integrating with existing identity infrastructure","Security-sensitive applications requiring fine-grained authorization"],"limitations":["Auth implementation is manual; no built-in OAuth2 flows or SAML support (contrib extensions available)","Authorization is tool-level; fine-grained resource-level authorization requires custom middleware","Token refresh and expiration handling is manual; no automatic token lifecycle management","Auth state is request-scoped; cross-request auth state (sessions) requires external session store"],"requires":["Python 3.9+","FastMCP server framework","Auth backend implementation (custom or from contrib extensions)","Credentials or tokens (API keys, OAuth tokens, JWT)"],"input_types":["Authentication credentials (API key, token, username/password)","Authorization policies (role-based, attribute-based)","Custom auth backend implementations"],"output_types":["Authenticated user context","Authorization decision (allow/deny)","User metadata and roles"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_11","uri":"capability://automation.workflow.tool.transformation.and.validation.pipeline.with.custom.transforms","name":"tool transformation and validation pipeline with custom transforms","description":"FastMCP provides a transformation system that allows tools to be modified or wrapped with custom logic before execution. Transforms can validate inputs, sanitize outputs, add logging, implement retry logic, or modify tool behavior. Transforms are composable and can be applied at the server level (affecting all tools) or per-tool, enabling uniform behavior modification without changing tool definitions.","intents":["I want to add input validation or sanitization to all tools without modifying tool code","I need to implement retry logic, timeout handling, or error recovery for tools","I want to log or audit all tool executions for compliance or debugging"],"best_for":["High-reliability systems requiring retry logic and error handling","Compliance-focused applications needing audit trails and input validation","Teams implementing cross-cutting tool behavior without modifying tool definitions"],"limitations":["Transforms add execution overhead; complex transforms can add 10-50ms per tool call","Transform composition order matters; incorrect ordering can cause unexpected behavior","Transforms operate on tool inputs/outputs; they cannot modify tool execution semantics","No built-in transforms for common concerns; teams must implement custom transforms or use contrib extensions"],"requires":["Python 3.9+","FastMCP server framework","Custom transform implementations"],"input_types":["Tool definitions","Tool parameters","Tool execution results"],"output_types":["Transformed tool definitions","Validated/sanitized parameters","Transformed results"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_12","uri":"capability://automation.workflow.caching.middleware.for.tool.results.with.configurable.ttl.and.invalidation","name":"caching middleware for tool results with configurable ttl and invalidation","description":"FastMCP provides a caching middleware that caches tool execution results based on input parameters. The cache supports configurable time-to-live (TTL), manual invalidation, and cache key customization. Caching is transparent to tools and can be applied selectively to expensive operations, reducing redundant computation and improving response latency for repeated requests.","intents":["I want to cache results from expensive tools (API calls, database queries) to reduce latency","I need to invalidate cached results when underlying data changes","I want to reduce load on backend services by caching tool results"],"best_for":["High-traffic MCP servers with expensive tool operations","Systems with read-heavy workloads where caching provides significant latency reduction","Applications where eventual consistency is acceptable"],"limitations":["Cache is in-memory by default; distributed caching requires external cache backend (Redis)","Cache invalidation is manual; no automatic invalidation based on data dependencies","Cache key generation is based on input parameters; tools with side effects may produce incorrect cached results","Cache size is unbounded by default; memory exhaustion possible with large result sets or long TTLs"],"requires":["Python 3.9+","FastMCP server with caching middleware enabled","Optional: Redis or similar cache backend for distributed caching"],"input_types":["Tool parameters (used as cache key)","Cache TTL configuration","Cache invalidation signals"],"output_types":["Cached tool results","Cache hit/miss metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_13","uri":"capability://automation.workflow.server.composition.and.mounting.with.hierarchical.tool.organization","name":"server composition and mounting with hierarchical tool organization","description":"FastMCP supports composing multiple MCP servers into a single logical server through mounting. Mounted servers are exposed as namespaced tool groups, allowing hierarchical organization of tools (e.g., /database/*, /api/*, /files/*). This enables modular server architecture where different teams can develop and deploy independent tool providers that are composed at runtime.","intents":["I want to organize tools into logical groups or namespaces without creating separate servers","I need to compose tools from multiple independent providers into a single MCP server","I want different teams to develop tool providers independently and compose them at runtime"],"best_for":["Large organizations with multiple teams developing tool providers","Modular architectures where tool providers are developed and deployed independently","Systems requiring hierarchical tool organization for discoverability"],"limitations":["Mounted servers are composed at initialization; dynamic mounting/unmounting requires server restart","Tool name collisions across mounted servers are not automatically resolved; manual namespacing required","Mounted servers share the same context and middleware; isolation requires custom middleware","Performance overhead of composition; each mounted server adds routing overhead"],"requires":["Python 3.9+","FastMCP server framework","Multiple MCP server instances to compose"],"input_types":["MCP server instances","Mount paths (namespaces)","Composition configuration"],"output_types":["Composed server with namespaced tools","Hierarchical tool organization"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_14","uri":"capability://automation.workflow.proxy.server.architecture.for.oauth2.and.multi.server.orchestration","name":"proxy server architecture for oauth2 and multi-server orchestration","description":"FastMCP provides a proxy server pattern (src/fastmcp/server/proxy.py) that acts as an intermediary between clients and backend MCP servers. The proxy can implement OAuth2 flows, request routing, authentication delegation, and multi-server orchestration. This enables centralized auth management, load balancing, and protocol translation without modifying backend servers.","intents":["I want to add OAuth2 authentication to existing MCP servers without modifying them","I need to route requests to different backend servers based on user or request context","I want to implement a central auth gateway for multiple MCP servers"],"best_for":["Organizations deploying multiple MCP servers requiring centralized auth","Systems needing OAuth2 flows or complex auth orchestration","Multi-tenant platforms routing requests to tenant-specific servers"],"limitations":["Proxy adds latency to every request; not suitable for latency-sensitive applications","Proxy is a single point of failure; high availability requires load balancing and replication","OAuth2 implementation is manual; no built-in OAuth2 provider integration","Request routing logic must be custom-implemented; no automatic routing based on request attributes"],"requires":["Python 3.9+","FastMCP proxy module","Backend MCP servers","OAuth2 provider (for OAuth2 flows)"],"input_types":["Client requests","OAuth2 tokens or credentials","Routing configuration"],"output_types":["Proxied requests to backend servers","Aggregated responses","Auth tokens and session state"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_15","uri":"capability://automation.workflow.cli.tooling.for.server.development.testing.and.deployment","name":"cli tooling for server development, testing, and deployment","description":"FastMCP provides a command-line interface for developing, testing, and deploying MCP servers. The CLI supports running servers locally, testing tool definitions, inspecting server capabilities, and generating configuration files. The CLI integrates with the FastMCP framework to provide development-time feedback and validation without requiring manual server startup or client setup.","intents":["I want to test my MCP server tools locally without setting up a client","I need to inspect available tools, resources, and prompts in my server","I want to validate tool definitions and catch schema errors before deployment"],"best_for":["Developers building and testing MCP servers","Teams validating server configurations before deployment","Rapid prototyping and iteration on tool definitions"],"limitations":["CLI testing is interactive; automated testing requires programmatic client API","CLI output is text-based; complex tool results may be difficult to inspect","CLI does not support all MCP features (e.g., streaming, complex authentication)","CLI is local-only; remote server testing requires network configuration"],"requires":["Python 3.9+","FastMCP CLI installed","MCP server code or configuration file"],"input_types":["Server module or configuration file","Tool parameters (for testing)","CLI commands and flags"],"output_types":["Server capabilities (tools, resources, prompts)","Tool execution results","Validation errors and warnings"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_16","uri":"capability://automation.workflow.configuration.management.with.environment.based.settings.and.multi.server.support","name":"configuration management with environment-based settings and multi-server support","description":"FastMCP provides a configuration system (MCPServerConfig, MCPConfig) that supports environment-based settings, multi-server configurations, and deployment-specific overrides. Configuration can be loaded from environment variables, YAML files, or Python code, enabling flexible deployment across development, staging, and production environments. The configuration system integrates with the uv package manager for reproducible dependency management.","intents":["I want to configure my MCP server differently for development, staging, and production","I need to manage multiple MCP servers with different configurations","I want to load configuration from environment variables or config files"],"best_for":["Teams deploying MCP servers across multiple environments","Organizations managing multiple MCP servers with different configurations","DevOps teams automating server deployment and configuration"],"limitations":["Configuration is static at server startup; runtime configuration changes require server restart","Configuration validation is basic; complex validation logic requires custom implementation","No built-in secrets management; sensitive configuration requires external secrets store","Multi-server configuration is file-based; dynamic server addition requires file updates and restart"],"requires":["Python 3.9+","FastMCP configuration module","Configuration file (YAML) or environment variables"],"input_types":["Environment variables","YAML configuration files","Python configuration objects"],"output_types":["Parsed configuration","Server instances with applied configuration","Validation errors"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_2","uri":"capability://memory.knowledge.context.and.dependency.injection.system.for.request.scoped.state.management","name":"context and dependency injection system for request-scoped state management","description":"FastMCP provides a context system (via src/fastmcp/server/context.py) that manages request-scoped state and dependency injection for tool handlers. The framework uses Python's contextvars for thread-safe, async-safe state propagation, allowing tools to access session data, authentication context, and injected dependencies without explicit parameter passing through the call chain.","intents":["I need to access user session or authentication context within tool handlers without passing it through function parameters","I want to inject database connections or service instances into tools at request time","I need to track request metadata (user ID, trace ID) across async tool execution"],"best_for":["Multi-tenant MCP servers requiring per-request isolation","Applications with complex dependency graphs (database, cache, external services)","Async-heavy architectures where contextvars provide cleaner state management than parameter passing"],"limitations":["Context is thread-local and async-task-local; context does not propagate across process boundaries or thread pools without explicit management","Dependency injection requires manual registration; no automatic discovery or autowiring of dependencies","Context cleanup must be explicit; improper context management can lead to state leakage between requests"],"requires":["Python 3.9+ (contextvars support)","FastMCP server with context middleware enabled","Understanding of async context propagation semantics"],"input_types":["Context values (user ID, session token, request metadata)","Dependency instances (database connections, service objects)","Async callable factories for lazy dependency initialization"],"output_types":["Request-scoped state accessible within tool handlers","Injected service instances","Cleanup callbacks for resource management"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_3","uri":"capability://tool.use.integration.provider.based.resource.and.tool.composition.with.aggregation","name":"provider-based resource and tool composition with aggregation","description":"FastMCP implements a provider pattern (src/fastmcp/server/providers/) where tools, resources, and prompts can be sourced from multiple providers (FastMCP decorators, OpenAPI specs, filesystem, custom implementations) and aggregated into a single server. Providers implement a common interface and are composed via an AggregateProvider, enabling modular tool definition and dynamic capability composition without monolithic server code.","intents":["I want to compose tools from multiple sources (decorated functions, OpenAPI specs, external services) into a single MCP server","I need to dynamically add or remove capabilities without restarting the server","I want to expose existing REST APIs as MCP tools without rewriting them"],"best_for":["Teams building integration platforms that aggregate multiple tool sources","Enterprises exposing legacy REST APIs as MCP capabilities","Modular architectures where tools are defined in separate packages or services"],"limitations":["Provider composition is static at server initialization; dynamic provider addition requires server restart or complex hot-reload logic","OpenAPI provider has limited support for complex authentication schemes (OAuth2 flows, mTLS); basic auth and API keys work reliably","Tool name collisions across providers are not automatically resolved; manual namespacing required"],"requires":["Python 3.9+","FastMCP server framework","OpenAPI 3.0 spec (for OpenAPI provider)","Filesystem access (for filesystem provider)"],"input_types":["FastMCP decorated functions","OpenAPI 3.0 specification documents (JSON/YAML)","Filesystem paths containing tool definitions","Custom provider implementations"],"output_types":["Aggregated tool definitions","Unified resource catalog","Composite prompt templates","MCP protocol messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_4","uri":"capability://tool.use.integration.openapi.to.mcp.tool.transformation.with.automatic.schema.mapping","name":"openapi-to-mcp tool transformation with automatic schema mapping","description":"FastMCP includes an OpenAPI provider that parses OpenAPI 3.0 specifications and automatically transforms REST endpoints into MCP tools. The provider maps OpenAPI parameters, request bodies, and response schemas to MCP tool definitions, handling content negotiation, authentication headers, and response parsing transparently. This enables exposing existing REST APIs as MCP-compatible tools without manual tool definition.","intents":["I want to expose my REST API as MCP tools without writing custom tool handlers","I need to make legacy REST services accessible to LLM agents through MCP","I want to automatically generate MCP tools from OpenAPI documentation"],"best_for":["Teams with existing REST APIs seeking MCP integration without code changes","API platform teams building multi-protocol access layers","Rapid prototyping of agent-accessible APIs from OpenAPI specs"],"limitations":["OpenAPI provider supports basic and API key authentication; OAuth2 flows and complex auth schemes require custom implementation","Streaming responses and WebSocket endpoints are not supported; only request-response patterns work","Large response bodies may exceed MCP message size limits; pagination must be manually configured","OpenAPI specs with circular references or deeply nested schemas may produce oversized tool definitions"],"requires":["Python 3.9+","OpenAPI 3.0 specification (JSON or YAML format)","Network access to REST API endpoints","FastMCP OpenAPI provider module"],"input_types":["OpenAPI 3.0 specification document","API endpoint URL","Authentication credentials (API key, basic auth)"],"output_types":["MCP tool definitions derived from OpenAPI operations","Mapped parameter schemas","Response content as structured data"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_5","uri":"capability://memory.knowledge.resource.and.template.system.for.exposing.structured.data.and.dynamic.content","name":"resource and template system for exposing structured data and dynamic content","description":"FastMCP provides a resource system (@mcp.resource decorator) that exposes structured data and templates to LLMs. Resources can be static files, dynamic content generated from templates, or computed values. The framework supports URI-based resource addressing with template variables, allowing LLMs to request specific resource instances (e.g., /file/{path}) and receive content with automatic MIME type detection and encoding.","intents":["I want to expose files, documents, or structured data to LLMs for analysis or context","I need to provide dynamic content templates that LLMs can instantiate with parameters","I want to give LLMs read-only access to specific data sources without exposing APIs"],"best_for":["Knowledge management systems exposing documents to LLM agents","Code analysis tools providing source files and documentation to LLMs","Data platforms offering read-only data access for LLM context"],"limitations":["Resources are read-only; no write capability through the resource interface","Large resources (>10MB) may exceed LLM context windows; pagination or summarization required","Template variables are limited to simple string substitution; complex transformations require custom resource handlers","No built-in caching; frequently accessed resources generate repeated computation"],"requires":["Python 3.9+","FastMCP server framework","Resource content (files, templates, or callable generators)"],"input_types":["File paths or URIs","Template strings with variable placeholders","Callable functions returning resource content"],"output_types":["Resource content (text, JSON, binary)","MIME type metadata","Resource URI and description"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_6","uri":"capability://text.generation.language.prompt.system.for.exposing.llm.optimized.instruction.templates","name":"prompt system for exposing llm-optimized instruction templates","description":"FastMCP provides a prompt system (@mcp.prompt decorator) that exposes reusable instruction templates to LLMs. Prompts are parameterized text templates that can be instantiated with arguments, allowing servers to provide domain-specific guidance, system prompts, or few-shot examples. Prompts are discoverable by LLMs and can be dynamically selected based on task context.","intents":["I want to provide domain-specific system prompts or instructions to LLMs using my tools","I need to expose few-shot examples or prompt templates that guide LLM behavior","I want to centralize prompt management in the MCP server rather than in client code"],"best_for":["Specialized tool providers offering domain-specific guidance (code generation, data analysis, creative writing)","Multi-agent systems where different agents need different system prompts","Teams managing prompt versions and A/B testing prompt variants"],"limitations":["Prompts are text-based; no support for structured prompt formats or advanced prompt engineering techniques","Prompt discovery is manual; LLMs must explicitly request prompts rather than automatic selection","No built-in versioning or prompt A/B testing; version management requires external tooling","Prompt arguments are simple string substitution; complex logic requires custom prompt handlers"],"requires":["Python 3.9+","FastMCP server framework","Prompt template text with optional parameter placeholders"],"input_types":["Prompt template strings","Parameter values for template instantiation","Prompt metadata (name, description, arguments)"],"output_types":["Instantiated prompt text","Prompt metadata and argument specifications","MCP prompt definition messages"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_7","uri":"capability://automation.workflow.background.task.execution.with.async.await.support.and.session.state.persistence","name":"background task execution with async/await support and session state persistence","description":"FastMCP provides a background task system that allows tools to spawn long-running operations without blocking the MCP response. Tasks are executed asynchronously using Python's asyncio, with support for progress updates, cancellation, and result persistence. The framework manages task lifecycle and provides session state storage for maintaining state across task execution and client reconnections.","intents":["I want tools to spawn long-running operations (data processing, file uploads) without blocking the MCP response","I need to track task progress and allow clients to poll or subscribe to task status updates","I want to persist task results and state across client sessions"],"best_for":["Tools performing long-running operations (data processing, model training, file operations)","Interactive applications requiring progress feedback and cancellation support","Systems needing task persistence across client reconnections"],"limitations":["Background tasks are in-process; distributed task execution requires external task queue (Celery, RQ)","Task state is stored in-memory by default; persistence requires external storage backend configuration","No built-in task scheduling; recurring tasks require external scheduler integration","Task cancellation is cooperative; tasks must check cancellation flag and exit gracefully"],"requires":["Python 3.9+","FastMCP server with async support","asyncio event loop (automatic in FastMCP)","Optional: external state store for task persistence"],"input_types":["Async callable functions","Task parameters and configuration","Cancellation signals"],"output_types":["Task ID and status","Progress updates","Task results","Error information"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_8","uri":"capability://automation.workflow.middleware.system.for.request.response.interception.and.cross.cutting.concerns","name":"middleware system for request/response interception and cross-cutting concerns","description":"FastMCP provides a middleware system that allows interception of tool calls, resource requests, and prompt queries before and after execution. Middleware can implement cross-cutting concerns like authentication, authorization, logging, caching, rate limiting, and request/response transformation. Middleware is composed in a chain and executes in order, with each middleware able to modify requests, responses, or short-circuit execution.","intents":["I want to add authentication and authorization checks to all tool calls without modifying tool code","I need to implement caching, rate limiting, or request logging across all MCP operations","I want to transform requests or responses globally (e.g., sanitize inputs, format outputs)"],"best_for":["Multi-tenant MCP servers requiring per-request security and isolation","High-performance systems needing caching or rate limiting middleware","Observability-focused teams implementing logging, tracing, and monitoring"],"limitations":["Middleware execution adds latency to every request; complex middleware chains can add 50-200ms per request","Middleware order matters; incorrect ordering can cause subtle bugs or security issues","No built-in middleware for common concerns; teams must implement custom middleware or use contrib extensions","Middleware cannot access tool execution results before response serialization; post-execution transformation is limited"],"requires":["Python 3.9+","FastMCP server framework","Understanding of middleware patterns and request/response lifecycle"],"input_types":["Tool call requests with parameters","Resource requests with URIs","Prompt instantiation requests"],"output_types":["Modified requests (after pre-execution middleware)","Modified responses (after post-execution middleware)","Short-circuit responses (for early termination)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-prefecthq-fastmcp__cap_9","uri":"capability://tool.use.integration.http.server.integration.with.openapi.documentation.generation","name":"http server integration with openapi documentation generation","description":"FastMCP can expose MCP servers via HTTP using FastAPI or similar frameworks, automatically generating OpenAPI documentation from tool definitions. The HTTP integration maps MCP tool calls to REST endpoints, handles request/response serialization, and exposes OpenAPI 3.0 specs that describe all available tools. 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