modelcontextprotocol/python-sdk
Python Sdk
The Model Context Protocol SDK is a framework for building clients and servers that connect AI models to external data, tools, and resources using a standardized communication protocol. It provides the foundational libraries and interfaces necessary to establish reliable, transport-agnostic connections between AI agents and external systems, enabling seamless information retrieval and task automation.
The SDK distinguishes itself through a robust capability negotiation handshake that ensures compatibility between connected parties before exchanging messages. It supports a pluggable transport abstraction, allowing developers to choose between streaming or HTTP methods, and features unified resource aggregation that combines tools, prompts, and data from multiple independent sources into a single interface.
The framework covers a broad capability surface, including structured data schemas for input and output validation, asynchronous task orchestration with progress tracking, and secure authentication workflows. It also provides diagnostic utilities for protocol debugging, server lifecycle management, and multi-modal data processing to handle binary assets and media formats.
The project includes comprehensive documentation and testing utilities, such as in-memory connection support, to verify server implementations and protocol behavior during development.
Features
- Model Context Protocol - Provides the foundational library for building clients and servers that connect AI models to external systems.
- AI Integration Frameworks - Provides a standardized framework for connecting AI models to external data, tools, and resources.
- AI Protocol Extensions - Implements a standardized, transport-agnostic protocol for AI agent interoperability and context retrieval.
- Function Registries - Allows developers to define functions as tools that enable AI models to perform computations or trigger side effects.
- Model Context Protocol Connectivity - Provides standardized communication layers for interfacing AI models with external data services.
- Remote Invocations - Supports invoking tools hosted on connected servers by name while passing arguments and handling progress updates.
- Resource Exposure Frameworks - Defines standardized read-only interfaces that allow AI models to discover and access external data resources.
- AI Agent Servers - Exposes local data, executable tools, and prompt templates to AI applications through a unified server interface.
- Capability Negotiation Protocols - Establishes compatible communication contracts by exchanging feature sets and protocol versions during initialization.
- MCP Server Management - Enables applications to discover and interact with external servers, tools, and resources.
- AI Orchestration - Manages tool execution, prompt templates, and task lifecycles between AI applications and remote services.
- Aggregators - Composes resources, tools, and prompts from multiple independent servers into a single unified interface.
- AI Agent Frameworks - Official SDK for building MCP servers and clients.
- Development and Debugging Tools - Official Python library for developing MCP servers and clients.
- Resource Aggregators - Unifies fragmented tools, prompts, and data resources from multiple sources into a single interface.
- Data Schema Definitions - Generates standardized data schemas for validating tool inputs and outputs.
- AI Service Clients - Enables the development of applications that discover and interact with remote AI services and tools.
- Asynchronous Task Execution - Offloads long-running operations to the background with support for progress polling.
- Protocol Validation Tools - Ensures protocol version compatibility between clients and servers to prevent runtime errors.
- MCP Server Integrations - Mounts server instances into web frameworks to enable scalable, production-ready deployments.
- Sampling Request Handlers - Enables servers to request text completions from connected clients during tool execution.
- Task Hooks - Provides standardized hooks for reporting task progress, completion, and cancellation.
- OAuth Authentication - Manages OAuth authentication flows and token storage for secure resource access.
- Sensitive Data Access Controls - Implements authorization mechanisms to restrict access to sensitive tools and data.
- Asynchronous Task Orchestration - Orchestrates long-running background tasks with support for progress tracking and cancellation.
- Transport Abstractions - Decouples application logic from underlying communication media like stdio or HTTP through unified transport interfaces.
- Lifecycle Hooks - Manages resource initialization and cleanup during server startup and shutdown to ensure consistent data access.
- Prompt Templates - Supports reusable prompt templates that users can invoke to guide AI behavior.
- Pagination - Implements chunked retrieval for list operations to efficiently handle and serve extensive collections of resources.
- JSON-RPC Message Buses - Facilitates structured message exchange between clients and servers using the JSON-RPC protocol.
- Transport Configurations - Supports flexible transport configuration, including streaming and HTTP methods.
- State Change Broadcasting - Broadcasts updates to connected clients when resources or tools change to maintain state synchronization.
- Data Validation Schemas - Enforces strict input and output structures for tools and resources using type-safe schema definitions.
- RPC Cancellation - Notifies remote parties to abort ongoing operations to allow for efficient resource management when tasks are no longer needed.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
