modelcontextprotocol/go-sdk
Go Sdk
Este es un SDK y framework para implementar el Model Context Protocol en Go. Proporciona un sistema estandarizado para construir servidores y clientes que intercambian recursos externos, datos propietarios y herramientas ejecutables para proporcionar contexto a modelos de lenguaje grandes (LLM).
El SDK incluye una librería de comunicación JSON-RPC y un framework de integración para exponer datos locales, plantillas de prompts y funciones tipadas a modelos de IA. Permite el desarrollo tanto de servidores de protocolo que proporcionan contexto externo como de clientes que consumen estas herramientas y recursos remotos.
El proyecto cubre la gestión del ciclo de vida de la conexión y la negociación de versiones del protocolo para asegurar la interoperabilidad. Proporciona abstracciones de transporte para el intercambio de mensajes vía entrada/salida estándar o HTTP, junto con capacidades para el mapeo de recursos y la gestión de sesiones.
Las funciones de seguridad y observabilidad incluyen integración de identidad OAuth, restricciones de acceso a directorios para servidores y herramientas para la inspección de tráfico y verificación de capacidades.
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.
