mark3labs/mcp-go

Features

• Model Context Protocol - Implements the standardized Model Context Protocol to connect AI models with local data and external tools.
• Model Context Protocol Implementations - Implements the Model Context Protocol (MCP) to standardize communication between AI models and external tools.
• Agent Context Management - Manages agent context by serving static files and dynamic API data as resources for language models.
• LLM Tooling Integrations - Provides a framework for creating executable functions and tools that language models can trigger in external systems.
• MCP Servers - Implements the server side of the Model Context Protocol to expose tools and data via stdio, SSE, or HTTP.
• Resource Exposure - Exposes read-only static files and dynamic data as resources to serve as context for AI models.
• Transport Layer Connectivity - Supports multiple communication channels including stdio, HTTP, and SSE for reliable connectivity.
• Model Context Protocol Servers - Provides a framework for creating executable functions that allow language models to perform computations and trigger side effects.
• Bidirectional Protocol Communication - Enables bidirectional exchange of requests and notifications between servers and clients using HTTP POST and Server-Sent Events.
• JSON-RPC Implementations - Implements the JSON-RPC protocol to standardize the exchange of requests and responses between AI clients and servers.
• Go SDKs - Provides a comprehensive Go developer kit for implementing bidirectional communication between AI clients and servers.
• Conversation Context Tracking - Maintains unique client state to deliver targeted notifications and filtered capability lists based on session context.
• Middleware Extensions - Provides middleware extensions to intercept and modify protocol communication for logging and error recovery.
• Structured Tool Outputs - Returns complex data types using defined schemas to ensure precisely formatted output for language models.
• Prompt Templates - Provides capabilities to define reusable prompt templates with system instructions and arguments for guiding language models.
• AI Tool Schemas - Uses structured metadata to define precise function arguments and return types for language model execution.
• Dynamic Resource Templates - Maps unique resource identifiers to dynamic data generators using URI templates to provide model context.
• Client Session Management - Maintains unique states for connected clients to enable targeted notifications and personalized configurations.
• Protocol-Agnostic Transport Layers - Decouples protocol logic from underlying communication channels using a transport-agnostic message bus.
• Asynchronous Task Execution - Implements non-blocking background task execution for tools with polling mechanisms for result retrieval.
• Task Status Polling - Provides a polling mechanism for clients to retrieve results from long-running background operations.
• AI Protocol Observability - Tracks request lifecycles and tool executions with telemetry and spans to debug client-server interactions.
• Request Tracing - Tracks requests and tool executions using spans and attributes for performance debugging.
• AI Agent Frameworks - SDK for developing Model Context Protocol clients and servers.
• AI and Machine Learning - Go implementation of the Model Context Protocol.
• AI & Machine Learning - Implementation of the Model Context Protocol for Go.
• Development and Debugging Tools - Go language implementation for building efficient MCP servers.

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