Fastmcp

FastMCP is a Python framework designed for building servers that expose functions, resources, and prompts to AI models using the Model Context Protocol. It simplifies the development process by automatically deriving tool metadata, input schemas, and documentation directly from Python function signatures and type hints. The framework provides a unified container for managing these components, allowing developers to build modular applications that integrate seamlessly with AI assistants.

The project distinguishes itself through its support for interactive, server-defined user interface components that render directly within AI chat environments. It includes a dynamic middleware pipeline for injecting cross-cutting concerns like authentication and telemetry, alongside a protocol-agnostic transport layer that supports stdio, HTTP, and server-sent events. These capabilities allow for the creation of rich, stateful interactions that extend beyond simple text-based tool execution.

The framework covers a broad capability surface, including comprehensive support for authentication, authorization, and secure deployment. It provides tools for managing long-running tasks, background execution, and complex dependency injection, while offering built-in observability through logging, distributed tracing, and performance monitoring. Developers can also leverage built-in CLI scaffolding and hot-reloading to accelerate the development and testing of server-side logic.

FastMCP is distributed as a Python library, with documentation and tooling focused on streamlining the registration and configuration of local server instances for external AI clients.

Features

Tool Definitions & Registration - Automatically exposes Python functions as executable tools for AI models by generating metadata and schemas from function signatures.
MCP Server Management - Provides a framework for hosting and exposing tools, resources, and prompts to AI models via the Model Context Protocol.
AI Agent Tool Integrations - Connects language models to external systems and custom logic by wrapping Python functions in standardized, discoverable tool interfaces.
MCP Server Integrations - Exposes library documentation and tool definitions to AI assistants via protocol-based servers.
Model Context Protocol Servers - Builds and hosts servers that expose Python functions as tools, resources, and prompts for AI models to discover and execute.
Server-Defined UI Components - Provides interactive, server-defined user interface components that render directly within AI chat environments.
Tool Exposure Frameworks - Wraps Python functions into protocol-compliant tools and resources that allow language models to interact with external logic.
Function-Based Tool Definitions - Derives tool metadata and schemas automatically from function signatures and type hints.
Generative User Interfaces - Enables language models to generate and stream custom interactive user interface code that renders directly within chat environments.
Enterprise Deployment Platforms - MCP deploys and secures server instances with features including single sign-on, role-based access control, audit logging, and centralized registry management.
Tool Builders - Wraps Python functions into executable tools with automatic schema generation, type validation, and documentation parsing.
User Input Elicitation - Prompts users for structured input during tool execution to gather necessary context dynamically.
Agent Tool Integrations - Connects agents to external tools by exposing server-defined functions through a unified interface that supports multiple communication transports.
Interactive AI Interfaces - Creates rich, client-side UI components like dashboards and forms directly from server-side Python code for use within AI chat environments.
MCP Server Configurations - Loads server settings from configuration files to initiate processes with runtime overrides.
Tool Schema Definitions - Exposes functions as tools by automatically generating schemas and parsing docstrings.
AI Assistant Integrations - Connects custom server-defined tools, resources, and prompts to an assistant, allowing the model to execute local functions and access external data.
Server Capability Exposure - Publishes tools, resources, and prompts to clients to provide context and capabilities to connected AI assistants.
Agent-to-Server Bridges - Provides a command-line interface for non-native agents to discover and execute tools via structured data input and output.
Protocol-Agnostic Transport Layers - Decouples server logic from communication methods by supporting interchangeable transport backends like stdio, HTTP, and SSE.
OAuth Providers - Secures server access by validating OAuth tokens against configured identity providers.
Type-Hint-Based Schema Definitions - Automatically derives tool metadata, input schemas, and documentation from Python function signatures and type hints at runtime.
Backend-to-Frontend Bridges - Triggers server-side functions from the user interface to perform data operations and update application state dynamically.
Agentic Reasoning Loops - Enables models to autonomously call registered functions and process results iteratively to complete complex, multi-step tasks.
AI Agent Skills - Exposes local directories containing AI instructions and files as discoverable resources.
AI Agent Frameworks - Pythonic way to build MCP servers and clients.
Contextual Response Objects - Returns complex objects and message sequences to provide LLMs with rich, multi-turn context.
Backend Server Integration - Manages communication between client-side interface actions and server-side logic for form submissions and data persistence.
Request Middleware - Intercepts incoming requests and outgoing responses to inject cross-cutting concerns like authentication, logging, and telemetry.
Resource Exposure Interfaces - Maps resources and prompts to tool-based interfaces to ensure compatibility with clients that only support tool execution.
AI Access Control - Manages authentication, authorization, and access control for AI-accessible tools using OAuth, OIDC, and granular policy enforcement.
Assistant Integration Automations - Automates the configuration and registration of local server instances to expose custom tools and resources to the assistant.
Structured Return Objects - Serializes function return values into machine-readable JSON to allow client-side deserialization.
Code Execution Sandboxes - Executes LLM-generated Python code within isolated, resource-constrained sandbox environments.
Server Configuration Management - Uses centralized configuration files to define server identity, transport settings, and dependency management.
Protocol Bridges - Provides a communication layer for hosting servers over standard input/output, HTTP, or SSE to enable local and remote AI integrations.
Authentication Servers - Integrates Auth0 OAuth authentication into servers using an OIDC proxy pattern.
OAuth Authentication - Secures server access by validating Microsoft Entra ID tokens and managing OAuth flows.
Tool Exposure Interfaces - Exposes defined functions and prompts to interfaces so they can be invoked as tools during conversation.
Server Management - Executes setup and teardown logic during server startup and shutdown to manage shared context.
Dynamic Interface Renderers - Interprets structured component trees into functional user interfaces within chat environments.
Interactive Dashboards - Renders interactive data visualizations and performance metrics within the interface.
Interactive UI Components - Provides rich, client-side interactivity by rendering Python-defined components directly in conversations.
URI-Based Resource Definitions - Exposes data via URI-based endpoints, supporting both static content and dynamic functions.
Dual-Mode Result Presenters - Returns both visual UI and text-based summaries to support both user interaction and model reasoning.
Embedded Web Renderers - Embeds bespoke web applications within conversations to provide interactive visualizations for tool outputs.
Custom Tool Definitions - Enables defining custom providers by implementing interfaces that source tools and resources from external logic.
Modular Server Mounts - Builds complex applications by mounting multiple sub-servers into a single unified interface.
Human Approval - Adds confirmation gates to agent actions, allowing users to approve or reject operations via interactive buttons.
Human-in-the-loop Controls - Requires explicit user confirmation before executing sensitive agent actions to ensure safety.
LLM Provider Integrations - Connects to external model providers to handle sampling requests and tool use.
Aggregators - Composes multiple modular servers into a single unified interface with namespacing and dependency management.
Multi-Agent System Composition - Creates unified chat interfaces that aggregate tools and capabilities from multiple servers into a single, cohesive environment for interaction.
Tool Definition Frameworks - Provides a decorator-based API for registering Python functions as executable tools for AI models.
Docstring Metadata Extractors - Automatically derives tool metadata and documentation from Python function signatures and docstrings.
Media Response Encapsulators - Encapsulates images, audio, and files within tool responses to ensure models can interpret non-textual information.
OpenAPI-to-Tool Converters - Automatically generates functional server interfaces by parsing OpenAPI specifications.
URI Resource Identifiers - Retrieves static or templated data from servers using URI-based identifiers.
Interactive Execution Interfaces - Creates composable user interfaces for backend tools to facilitate direct function execution.
Server Metadata Registries - Maintains a centralized catalog of servers to organize and discover capabilities across an organization.
Access Authentication - Restricts access to server resources using OAuth tokens while maintaining client compatibility.
Access Tokens - Validates incoming authentication tokens by checking signatures, expiration, and claims.
API Access Security - Implements authentication mechanisms like OAuth and token verification to restrict access to server capabilities.
Authentication Clients - Configures client connections using Bearer tokens, OAuth flows, or metadata documents for secure access.
Bearer Token Authentication - Configures HTTP-based clients to include JSON Web Tokens in authorization headers.
Action Approval Policies - Interjects a confirmation step into agent workflows by presenting interactive buttons to users.
Access Control and Authorization - Enforces granular access control on tools, resources, and prompts by validating user scopes and claims.
Resource Access Policies - Defines settings for available tools and data resources to manage how they are exposed and utilized within the application environment.
Identity-Based Access Control - Validates incoming requests against external authentication providers to ensure only authorized users interact with tools and data.
Identity Token Services - Validates incoming tokens against Azure AD or Managed Identity providers.
OIDC Authentication Plugins - Integrates with OpenID Connect identity providers to delegate user authentication.
Policy-Based Access Control - Enforces granular access control by evaluating dynamic JSON-based policies against incoming requests.
Role-Based Access Control - Enforces authentication and granular access control at the tool level to govern agent interactions.
Component Access Policies - MCP controls which tools are available to clients by dynamically enabling or disabling components at runtime using blocklist or allowlist modes.
Data Validation Schemas - Generates validated data structures from schema definitions for use with validation frameworks.
Dependency Injection - Provides access to shared resources within a function by automatically resolving and passing required objects during execution.
Request Interception Middleware - Implements middleware for intercepting requests, responses, and notifications to handle cross-cutting concerns like logging and security.
Namespace-Aware Registries - Aggregates tools, resources, and prompts into a single namespace-aware container to simplify management and discovery.
UI Configuration Schemas - Attaches interactive UI configuration to tools using typed models for rich rendering.
Interface Interaction Management - Manages application state and interactive experiences directly within server-side code.
Schema-Based Generators - Constructs input forms and validation logic automatically based on data schemas.
Interactive Chat Interfaces - Generates interactive tool interfaces that display directly within the conversation flow.
Selection Lists - Displays interactive choices to users and captures selections to inform conversation flow.
UI State Management - Connects backend tools to frontend components to manage data-driven interactions and state updates.

Star 历史

PrefectHQfastmcp

Name: prefecthq/fastmcp
Author: PrefectHQ

View on GitHub

22,994 星标1,756 分支Pythonapache-2.019 次浏览gofastmcp.com

Fastmcp

FastMCP is distributed as a Python library, with documentation and tooling focused on streamlining the registration and configuration of local server instances for external AI clients.

Features

Tool Definitions & Registration - Automatically exposes Python functions as executable tools for AI models by generating metadata and schemas from function signatures.
MCP Server Management - Provides a framework for hosting and exposing tools, resources, and prompts to AI models via the Model Context Protocol.
AI Agent Tool Integrations - Connects language models to external systems and custom logic by wrapping Python functions in standardized, discoverable tool interfaces.
MCP Server Integrations - Exposes library documentation and tool definitions to AI assistants via protocol-based servers.
Model Context Protocol Servers - Builds and hosts servers that expose Python functions as tools, resources, and prompts for AI models to discover and execute.
Server-Defined UI Components - Provides interactive, server-defined user interface components that render directly within AI chat environments.
Tool Exposure Frameworks - Wraps Python functions into protocol-compliant tools and resources that allow language models to interact with external logic.
Function-Based Tool Definitions - Derives tool metadata and schemas automatically from function signatures and type hints.
Generative User Interfaces - Enables language models to generate and stream custom interactive user interface code that renders directly within chat environments.
Enterprise Deployment Platforms - MCP deploys and secures server instances with features including single sign-on, role-based access control, audit logging, and centralized registry management.
Tool Builders - Wraps Python functions into executable tools with automatic schema generation, type validation, and documentation parsing.
User Input Elicitation - Prompts users for structured input during tool execution to gather necessary context dynamically.
Agent Tool Integrations - Connects agents to external tools by exposing server-defined functions through a unified interface that supports multiple communication transports.
Interactive AI Interfaces - Creates rich, client-side UI components like dashboards and forms directly from server-side Python code for use within AI chat environments.
MCP Server Configurations - Loads server settings from configuration files to initiate processes with runtime overrides.
Tool Schema Definitions - Exposes functions as tools by automatically generating schemas and parsing docstrings.
AI Assistant Integrations - Connects custom server-defined tools, resources, and prompts to an assistant, allowing the model to execute local functions and access external data.
Server Capability Exposure - Publishes tools, resources, and prompts to clients to provide context and capabilities to connected AI assistants.
Agent-to-Server Bridges - Provides a command-line interface for non-native agents to discover and execute tools via structured data input and output.
Protocol-Agnostic Transport Layers - Decouples server logic from communication methods by supporting interchangeable transport backends like stdio, HTTP, and SSE.
OAuth Providers - Secures server access by validating OAuth tokens against configured identity providers.
Type-Hint-Based Schema Definitions - Automatically derives tool metadata, input schemas, and documentation from Python function signatures and type hints at runtime.
Backend-to-Frontend Bridges - Triggers server-side functions from the user interface to perform data operations and update application state dynamically.
Agentic Reasoning Loops - Enables models to autonomously call registered functions and process results iteratively to complete complex, multi-step tasks.
AI Agent Skills - Exposes local directories containing AI instructions and files as discoverable resources.
AI Agent Frameworks - Pythonic way to build MCP servers and clients.
Contextual Response Objects - Returns complex objects and message sequences to provide LLMs with rich, multi-turn context.
Backend Server Integration - Manages communication between client-side interface actions and server-side logic for form submissions and data persistence.
Request Middleware - Intercepts incoming requests and outgoing responses to inject cross-cutting concerns like authentication, logging, and telemetry.
Resource Exposure Interfaces - Maps resources and prompts to tool-based interfaces to ensure compatibility with clients that only support tool execution.
AI Access Control - Manages authentication, authorization, and access control for AI-accessible tools using OAuth, OIDC, and granular policy enforcement.
Assistant Integration Automations - Automates the configuration and registration of local server instances to expose custom tools and resources to the assistant.
Structured Return Objects - Serializes function return values into machine-readable JSON to allow client-side deserialization.
Code Execution Sandboxes - Executes LLM-generated Python code within isolated, resource-constrained sandbox environments.
Server Configuration Management - Uses centralized configuration files to define server identity, transport settings, and dependency management.
Protocol Bridges - Provides a communication layer for hosting servers over standard input/output, HTTP, or SSE to enable local and remote AI integrations.
Authentication Servers - Integrates Auth0 OAuth authentication into servers using an OIDC proxy pattern.
OAuth Authentication - Secures server access by validating Microsoft Entra ID tokens and managing OAuth flows.
Tool Exposure Interfaces - Exposes defined functions and prompts to interfaces so they can be invoked as tools during conversation.
Server Management - Executes setup and teardown logic during server startup and shutdown to manage shared context.
Dynamic Interface Renderers - Interprets structured component trees into functional user interfaces within chat environments.
Interactive Dashboards - Renders interactive data visualizations and performance metrics within the interface.
Interactive UI Components - Provides rich, client-side interactivity by rendering Python-defined components directly in conversations.
URI-Based Resource Definitions - Exposes data via URI-based endpoints, supporting both static content and dynamic functions.
Dual-Mode Result Presenters - Returns both visual UI and text-based summaries to support both user interaction and model reasoning.
Embedded Web Renderers - Embeds bespoke web applications within conversations to provide interactive visualizations for tool outputs.
Custom Tool Definitions - Enables defining custom providers by implementing interfaces that source tools and resources from external logic.
Modular Server Mounts - Builds complex applications by mounting multiple sub-servers into a single unified interface.
Human Approval - Adds confirmation gates to agent actions, allowing users to approve or reject operations via interactive buttons.
Human-in-the-loop Controls - Requires explicit user confirmation before executing sensitive agent actions to ensure safety.
LLM Provider Integrations - Connects to external model providers to handle sampling requests and tool use.
Aggregators - Composes multiple modular servers into a single unified interface with namespacing and dependency management.
Multi-Agent System Composition - Creates unified chat interfaces that aggregate tools and capabilities from multiple servers into a single, cohesive environment for interaction.
Tool Definition Frameworks - Provides a decorator-based API for registering Python functions as executable tools for AI models.
Docstring Metadata Extractors - Automatically derives tool metadata and documentation from Python function signatures and docstrings.
Media Response Encapsulators - Encapsulates images, audio, and files within tool responses to ensure models can interpret non-textual information.
OpenAPI-to-Tool Converters - Automatically generates functional server interfaces by parsing OpenAPI specifications.
URI Resource Identifiers - Retrieves static or templated data from servers using URI-based identifiers.
Interactive Execution Interfaces - Creates composable user interfaces for backend tools to facilitate direct function execution.
Server Metadata Registries - Maintains a centralized catalog of servers to organize and discover capabilities across an organization.
Access Authentication - Restricts access to server resources using OAuth tokens while maintaining client compatibility.
Access Tokens - Validates incoming authentication tokens by checking signatures, expiration, and claims.
API Access Security - Implements authentication mechanisms like OAuth and token verification to restrict access to server capabilities.
Authentication Clients - Configures client connections using Bearer tokens, OAuth flows, or metadata documents for secure access.
Bearer Token Authentication - Configures HTTP-based clients to include JSON Web Tokens in authorization headers.
Action Approval Policies - Interjects a confirmation step into agent workflows by presenting interactive buttons to users.
Access Control and Authorization - Enforces granular access control on tools, resources, and prompts by validating user scopes and claims.
Resource Access Policies - Defines settings for available tools and data resources to manage how they are exposed and utilized within the application environment.
Identity-Based Access Control - Validates incoming requests against external authentication providers to ensure only authorized users interact with tools and data.
Identity Token Services - Validates incoming tokens against Azure AD or Managed Identity providers.
OIDC Authentication Plugins - Integrates with OpenID Connect identity providers to delegate user authentication.
Policy-Based Access Control - Enforces granular access control by evaluating dynamic JSON-based policies against incoming requests.
Role-Based Access Control - Enforces authentication and granular access control at the tool level to govern agent interactions.
Component Access Policies - MCP controls which tools are available to clients by dynamically enabling or disabling components at runtime using blocklist or allowlist modes.
Data Validation Schemas - Generates validated data structures from schema definitions for use with validation frameworks.
Dependency Injection - Provides access to shared resources within a function by automatically resolving and passing required objects during execution.
Request Interception Middleware - Implements middleware for intercepting requests, responses, and notifications to handle cross-cutting concerns like logging and security.
Namespace-Aware Registries - Aggregates tools, resources, and prompts into a single namespace-aware container to simplify management and discovery.
UI Configuration Schemas - Attaches interactive UI configuration to tools using typed models for rich rendering.
Interface Interaction Management - Manages application state and interactive experiences directly within server-side code.
Schema-Based Generators - Constructs input forms and validation logic automatically based on data schemas.
Interactive Chat Interfaces - Generates interactive tool interfaces that display directly within the conversation flow.
Selection Lists - Displays interactive choices to users and captures selections to inform conversation flow.
UI State Management - Connects backend tools to frontend components to manage data-driven interactions and state updates.

Fastmcp 的开源替代方案

相似的开源项目，按与 Fastmcp 的功能重合度排序。

vercel/ai
vercel/ai
21,885在 GitHub 上查看
This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I
TypeScriptanthropicartificial-intelligencegemini
在 GitHub 上查看21,885
modelcontextprotocol/modelcontextprotocol
modelcontextprotocol/modelcontextprotocol
8,458在 GitHub 上查看
Model Context Protocol is a standardized framework for connecting large language models to external data sources and executable tools. It enables the creation of a universal interface where servers expose tools, resources, and prompts that can be discovered and utilized by various AI clients. The protocol utilizes a JSON-RPC message system that is transport-agnostic, supporting both standard input/output for local processes and HTTP with server-sent events for remote connections. It emphasizes security and control by delegating model sampling to the client to keep API keys secure from servers
TypeScript
在 GitHub 上查看8,458
modelcontextprotocol/typescript-sdk
modelcontextprotocol/typescript-sdk
12,674在 GitHub 上查看
This project provides a TypeScript software development kit for the Model Context Protocol, a standard designed to facilitate bidirectional communication between AI applications and external data sources or tools. It serves as a foundational framework for building both clients and servers, enabling language models to interact with external systems through a unified, decoupled interface. The SDK distinguishes itself by implementing a transport-agnostic connection layer that supports both local standard input-output streams and remote HTTP endpoints. It utilizes a JSON-RPC message bus to manage
TypeScript
在 GitHub 上查看12,674
tadata-org/fastapi_mcp
tadata-org/fastapi_mcp
11,560在 GitHub 上查看
This framework serves as a bridge between backend services and AI agents by implementing the Model Context Protocol. It enables developers to expose existing application logic and web endpoints as standardized tools, allowing AI models to discover, interact with, and execute backend functions through a unified interface. The project distinguishes itself by automatically converting application request and response models into protocol-compliant schemas, ensuring that AI agents receive accurate functional context. It supports a transport-agnostic architecture that facilitates real-time bidirect
Pythonaiauthenticationauthorization
在 GitHub 上查看11,560

查看 Fastmcp 的所有 30 个替代方案

常见问题解答

prefecthq/fastmcp 是做什么的？

prefecthq/fastmcp 的主要功能有哪些？

prefecthq/fastmcp 的主要功能包括：Tool Definitions & Registration, MCP Server Management, AI Agent Tool Integrations, MCP Server Integrations, Model Context Protocol Servers, Server-Defined UI Components, Tool Exposure Frameworks, Function-Based Tool Definitions。

prefecthq/fastmcp 有哪些开源替代品？

prefecthq/fastmcp 的开源替代品包括： vercel/ai — This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for… modelcontextprotocol/modelcontextprotocol — Model Context Protocol is a standardized framework for connecting large language models to external data sources and… modelcontextprotocol/typescript-sdk — This project provides a TypeScript software development kit for the Model Context Protocol, a standard designed to… tadata-org/fastapi_mcp — This framework serves as a bridge between backend services and AI agents by implementing the Model Context Protocol.… openai/openai-agents-python — This project is a Python framework for building autonomous, event-driven agent systems. It provides a unified runtime… modelcontextprotocol/inspector — The inspector is a diagnostic and validation tool for the Model Context Protocol. It provides an interactive interface…