tadata-org/fastapi_mcp
Fastapi Mcp
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 bidirectional communication via persistent HTTP streams and event-based methods, while allowing for the independent mounting of server interfaces to decouple communication layers from core service logic.
The framework includes comprehensive configuration options for managing service identity and filtering exposed operations to control access. It integrates directly with existing authentication and authorization flows to verify request identity, ensuring that only authorized entities can interact with the exposed tools. The library is designed to maintain compatibility with both modern streaming transports and legacy assistant implementations.
Features
- AI Agent Tool Integrations - Exposes application logic and backend functions as standardized tools for AI agents to discover and execute.
- MCP Server Integrations - Wraps existing application interfaces to serve them as standardized protocol endpoints for seamless integration with external assistants.
- Model Context Protocol Servers - Provides server-side implementations of the Model Context Protocol to enable seamless communication between AI assistants and internal services.
- Agent Tooling Protocols - Transforms application logic into standardized protocol tools, allowing external agents to discover and invoke backend functions.
- Agent-to-Server Bridges - Transforms standard web API endpoints into interactive tools that allow AI models to perform actions and retrieve data.
- AI Agent Servers - Acts as a bridge between backend services and AI models by providing standardized transport methods for agent connectivity.
- Tool Schema Definitions - Provides detailed tool schema definitions to help AI models interact effectively with API functions.
- Request Authentication Middleware - Integrates with authentication and authorization flows to verify agent identity before executing requests against exposed tools.
- Protocol Schema Mappers - Automatically converts application request and response models into protocol-compliant schemas to provide AI agents with accurate functional context.
- API Documentation Tools - Generates and serves standardized API documentation and schema definitions to provide AI agents with accurate functional context.
- Protocol Adapters - Wraps existing application logic into standardized interface definitions to allow external agents to interact with internal functions.
- HTTP-to-Function Adapters - Exposes application functionality through standard streamable HTTP protocols for robust session management.
- Framework - Maps existing web application endpoints and schemas to protocol-compliant tool definitions for automated agent interaction.
- Real-time Event Streams - Utilizes persistent HTTP connections to facilitate real-time bidirectional communication between services and external assistants.
- Server-Sent Events - Facilitates real-time interaction and continuous data exchange with external clients using standard HTTP streams.
- Authenticated Proxy Exposure - Protects internal application functionality with authentication and access policy enforcement for AI-driven service exposure.
- Schema-Driven Generators - Automatically converts application request and response models into protocol-compliant schemas for AI agents.
- Dynamic Endpoint Synchronizers - Dynamically updates server configurations to ensure AI assistants can discover and access updated application functionality.
- Dynamic Provider Discovery - Monitors application route changes at runtime to update exposed service configurations for continuous AI agent compatibility.
- Protocol-Agnostic Transport Layers - Decouples communication mechanisms from application logic to support both local streams and remote HTTP-based connections.
- Tool Access Authenticators - Integrates with existing security protocols to protect server functions and ensure only permitted entities execute requests against tools.
- Request Access Restrictions - Verifies incoming requests using standard authentication flows to ensure only authorized clients access exposed endpoints.
- Component Exposure Filters - Filters exposed API endpoints by operation identifiers or tags to control which functions are accessible to AI agents.
- Application Mounts - Allows attaching application interfaces to separate host processes to decouple protocol servers from core logic.
- HTTP Transport Configurations - Supports selecting between direct local communication and custom remote clients for reliable connectivity.
- Metadata Configurations - Allows assigning custom names and descriptions to the server to help AI assistants identify service capabilities.
- API Endpoint Mapping Guides - Maps application routes to specific URL paths to organize service accessibility for external agents.
