FastMCP is the leading Python framework for building production-ready Model Context Protocol (MCP) servers and clients. It simplifies connecting LLMs to tools and data by handling protocol complexity, serialization, and validation, allowing developers to focus on business logic. It's the standard for 70% of MCP servers.
Free
How to use FastMCP 3.0?
Install FastMCP via pip, then use its clean Pythonic API to decorate your functions as MCP tools. Define your components (tools, resources, prompts), configure providers for data sources, and apply transforms for client-specific views. Run your server locally or deploy it for free on Prefect Horizon to instantly connect your custom logic to AI agents like Claude Desktop.
FastMCP 3.0 's Core Features
Standard Framework: The de facto standard for building MCP applications, powering 70% of all MCP servers with a clean, Pythonic API.
Production-Ready: Handles all protocol complexities like serialization, validation, error handling, and compliance, making best practices the default.
Three Core Abstractions: Build with Components (tools/resources/prompts), Providers (data sources), and Transforms (client-side shaping) for maximum flexibility.
LLM-Optimized Workflow: Designed from the ground up to give AI agents the right information at the right time, preventing overload or under-provisioning.
Extensive Integration: Seamlessly integrates with decorated functions, local files, OpenAPI specs, remote servers, and major AI SDKs and assistants.
Free Hosting: Offers free deployment and hosting through Prefect Horizon, removing infrastructure barriers for developers.
Live Documentation: Unique feature where the product's own documentation is accessible via an MCP server, allowing AI agents to search and learn about FastMCP.
FastMCP 3.0 's Use Cases
AI Tool Developers: Enables developers to quickly wrap existing Python functions and APIs as tools for AI agents like Claude, reducing integration time from days to minutes.
Enterprise AI Teams: Allows large organizations to securely expose internal tools and data sources to LLMs with built-in authorization, namespacing, and versioning controls.
SaaS Platforms: Helps SaaS companies build AI-powered features by connecting their application's core functionality to LLMs via a standardized, maintainable protocol.
AI Researchers: Provides a robust framework for experimenting with tool-augmented LLMs, allowing rapid prototyping of new agent capabilities and data interactions.
DevOps Engineers: Simplifies the deployment and management of MCP servers at scale with support for HTTP deployment, configuration, and free cloud hosting.
