MCP Server Inspector and Debugger

The inspector is a diagnostic and validation tool for the Model Context Protocol. It provides an interactive interface and a transport proxy to discover, inspect, and execute the tools, prompts, and resources provided by an MCP server. The project serves as a debugger and compliance tester to verify that server implementations adhere to the protocol specification and JSON-RPC standards. It allows for real-time monitoring of message exchanges and logs between clients and servers across various transport layers, such as standard input/output and Server-Sent Events. The tool covers a broad rang

mcp-use is a development framework designed for building, deploying, and managing servers, clients, and autonomous agents using the Model Context Protocol. It provides a comprehensive toolkit for creating servers that expose custom tools, data resources, and prompts to compatible AI agents. The project distinguishes itself by offering a complete lifecycle for protocol-based applications, including a dedicated hosting platform for production servers and a compliance validator to ensure servers meet marketplace publishing requirements. It also features an observability suite for tracing protoco

mcp-go is a Go implementation of the Model Context Protocol (MCP) providing an SDK and framework for building servers that connect large language model applications to external tools and data sources. It serves as a developer kit for implementing bidirectional communication and structured data exchange between AI clients and servers. The framework enables the creation of executable tools with structured output schemas, reusable prompt templates, and data resource exposure via URI templates. It supports multiple transport layers, including stdio, HTTP, and Server-Sent Events, using a transport

Mempalace is a long-term memory management system for large language models that orchestrates the storage and retrieval of conversation history and entity relationships. It functions as a memory orchestrator and Model Context Protocol server, providing AI clients with read and write access to structured knowledge. The system utilizes a temporal knowledge graph to track evolving entity relationships and timelines with validity windows. It employs a hierarchical memory partitioning strategy, organizing data into wings and rooms to isolate specialist agent contexts and restrict semantic searches

This project is a Model Context Protocol server that provides large language models with neural web search and webpage content extraction capabilities. It implements a standardized interface to expose research tools and resources to compatible clients. The server integrates a neural search engine to retrieve real-time internet data using semantic embeddings rather than keyword matching. It includes specialized utilities for company intelligence and reasoning-based deep research, enabling the collection and synthesis of organizational data and professional profiles. The system covers a broad

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

The Model Context Protocol C# SDK is a library for building clients and servers that implement the Model Context Protocol to integrate AI tools and resources. It provides an AI tool integration framework and a multi-modal content handler to exchange text, images, and binary resources between AI models and external context providers. The SDK utilizes a JSON-RPC communication library to manage bidirectional data exchange. It features a transport-agnostic communication layer that supports standard input and output, HTTP, and in-memory pipes, with specific integration for ASP.NET Core hosting. T

This project is a tool for integrating existing HTTP APIs with AI agents by translating standard web endpoints into the Model Context Protocol. It provides a framework for constructing and managing libraries of functions that allow large language models to execute tasks and retrieve data. The system functions as an AI gateway that manages tool hosting, authentication, and routing. It includes capabilities for monetizing tool access through usage-based billing and payment processor integration, as well as the ability to publish service definitions to a gateway for commercial productization. T

This project is a curated library of community-driven prompt templates and personas designed to improve interactions with large language models. It functions as a prompt engineering guide, providing interactive tutorials and examples to teach advanced design and reasoning techniques. The library can operate as a Model Context Protocol server, providing a standardized interface for AI tools and agents to access prompt data as a service. For organizations, it offers a self-hosted repository option that allows for private deployment on internal infrastructure with custom authentication and data

This project is a Model Context Protocol server that connects large language models to the Xiaohongshu social media platform. It acts as a connector and API wrapper, enabling language models to programmatically search, read, and publish media and text. The system provides automation for content discovery and publishing, allowing for the creation of image and video posts with associated titles and descriptions. It also facilitates social engagement by managing the posting of comments and tracking engagement metrics for specific entries. The tool covers data retrieval for user profiles, post d

git-mcp is a Model Context Protocol server that transforms Git repositories and static sites into structured context providers for AI assistants. It functions as a documentation retrieval tool and repository indexer, exposing codebases and project files as standardized tools to reduce hallucinations in large language model responses. The project converts raw repository files, READMEs, and external URLs into formats optimized for token consumption. It enables AI agents to perform query-based code searches and retrieve specific sections of project documentation to maintain up-to-date technical