modelcontextprotocol/go-sdk

Features

• Model Context Protocol Clients - Provides the client-side integration layers that allow Go applications to connect to MCP servers and consume tools.
• Model Context Protocol Servers - Provides the core framework for implementing servers that expose tools, resources, and prompts to AI applications.
• Client Connection Lifecycles - Manages the complete session lifecycle, including initialization, capability negotiation, and termination between clients and servers.
• AI Tool and Resource Provisioning - Enables the creation of executable functions and read-only data interfaces for AI agents to interact with local files and services.
• API Client Connectivity - Provides the mechanisms for linking AI clients to local or remote servers.
• Client Development - Provides the development framework for building client applications that consume AI tools and data via a standardized interface.
• LLM Tooling Integrations - Provides a system for exposing local data, executable tools, and prompt templates to LLMs via a standardized protocol.
• Executable Tool Definitions - Provides standardized definitions for executable tools, including metadata and logic for LLM invocation.
• External Server Connectivity - Implements connectivity to external servers for the discovery and aggregation of AI tools.
• Prompt Templates - Provides tools for defining prompt templates that guide AI models on the use of specific tools and resources.
• Resource Exposure - Exposes read-only data resources, such as file contents or API responses, to be used as AI model context.
• AI Context Interfaces - Provides a standardized architecture for delivering external resources and proprietary data as context to AI models.
• Model Context Protocol Implementations - Provides a comprehensive Go SDK for building interoperable servers and clients that implement the Model Context Protocol.
• Prompt Templates - Implements systems for defining reusable prompt structures that guide users through tasks within a client application.
• AI Context Resource Management - Manages data resources accessed via unique URIs to provide grounding and context for LLM responses.
• Local Tool Exposure - Enables providing local data and tools to AI models through a standardized communication protocol.
• Context Data Sources - Provides structured, URI-addressable data sources that AI applications can retrieve and use as external context.
• Read-Only Resource Exposure - Provides mechanisms for sharing structured data from files or databases via unique identifiers.
• JSON-RPC Implementations - Implements the JSON-RPC protocol to enable structured request and notification exchanges between clients and servers.
• AI Protocol Server Integrations - Model Context Protocol support for creating custom servers that integrate proprietary tools and services via a standardized communication protocol.
• Model Tool Schemas - Maps executable functions to a standardized schema that allows large language models to discover and invoke tools.
• User Information Collection - Implements mechanisms for pausing AI operations to collect structured data from users via prompts.
• Local Server Launchers - Configures host applications to launch and communicate with local server instances for tool and resource access.
• LLM Completion Requests - Supports requesting text completions from a language model through a client while enforcing permission controls.
• Capability Negotiation Protocols - Implements protocols that manage the exchange and agreement of functional capabilities between clients and servers during initialization.
• MCP Transport Channels - Implements communication channels using standard input/output for local processes or HTTP for remote services.
• Version Negotiation - Handles version agreement between client and server during initialization to ensure protocol interoperability.
• Session Lifecycle Management - Coordinates the sequential states of an interactive session, from initialization handshakes to secure termination.
• Custom Transport Implementations - Allows replacing default transport layers with custom implementations like HTTP for web or container deployments.
• Transport Abstractions - Provides unified interfaces that decouple protocol logic from the underlying communication backends like HTTP or standard I/O.

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