The MCP-Framework is a TypeScript-based framework for elegantly building Model Context Protocol (MCP) servers. It provides an out-of-the-box architecture, supports automatic directory discovery of tools, resources, and prompts, and has multiple transmission methods and type-safe features.

DB MCP Server is a multi-database supported server that implements the Model Context Protocol (MCP) and provides AI assistants with the ability to access databases in a structured manner. It supports MySQL and PostgreSQL, can connect to multiple databases simultaneously, automatically generates dedicated tools, and follows a clear architecture design.

The Swarms API is a REST API for building, deploying, and managing multi-agent systems. It supports multiple AI models and swarm architectures, providing functions such as real-time monitoring, batch processing, and task scheduling.

A multi-platform job aggregation service based on the MCP architecture, supporting the acquisition and standardization of job data from mainstream recruitment websites and providing structured support for large models.

The GoHighLevel MCP server is a fundamental project aiming to connect the GoHighLevel community with AI automation through the Model Context Protocol (MCP). It provides access to all sub - account level GoHighLevel API endpoints, supports 269 tools, covering multiple functional categories such as contact management, messaging, sales pipelines, and calendar appointments. The project emphasizes community collaboration, an open architecture, and educational resources, while also reminding users to pay attention to key issues such as AI security, rate limits, and data security.

A model context protocol server implemented in C++, featuring a plugin-based architecture, supporting cross-platform operation, and providing modular expansion capabilities.

An MCP server implementation designed for the Claude desktop application, providing persistent memory functions for large language models, including a tiered memory architecture, multiple memory types, and semantic search features.

Edit-MCP is a model context protocol server integrated with the Microsoft Edit tool, providing advanced file editing capabilities for AI systems and adopting a hybrid architecture that combines direct file operations and Edit integration.

mcp - scaffold is a development sandbox for verifying the Model Context Protocol (MCP) server, providing support for local LLMs (such as LLaMA 7B) and cloud inference, including a chat interface and reference architecture.

MCP Serverless is a serverless architecture-based implementation of the model context protocol that provides a simple interface for tool management and request processing.

The Higress OPS MCP Server is a service implementation based on the Model Context Protocol (MCP) for configuring and managing the Higress gateway. The project also provides an MCP client based on LangGraph and LangChain MCP Adapters, which interacts with the Higress MCP Server through a proxy flow architecture.

MCP Server Semgrep is a server that complies with the Model Context Protocol standard, integrating the Semgrep static analysis tool with an AI assistant to provide code security analysis, quality improvement, and vulnerability detection functions. The project simplifies the architecture design, supports multi - platform operation, can be installed in multiple ways, and provides a rich set of code analysis functions.

Nano Agent is an experimental small engineering proxy MCP server that supports multi-provider LLM models, used to test and compare the proxy capabilities of cloud and local LLMs in terms of performance, speed, and cost. The project includes a multi-model evaluation system, a nested agent architecture, and a unified tool interface, supporting providers such as OpenAI, Anthropic, and Ollama.

The RevitMCP project introduces natural language interaction capabilities to Revit, realizes BIM modeling automation through the MCP protocol, adopts a hierarchical architecture design, supports multi - turn dialogue and parameter completion, and significantly improves modeling efficiency.

The Trello MCP Server is a server based on the Model Context Protocol, providing tools for interacting with the Trello API, including a complete Trello function interface, modular architecture, and type - safety support.

This project expands the video speech recognition function. It originally only supported the local Whisper model, and now it newly supports the online speech recognition services of CapCut and Bcut, providing a flexible multi - service selection architecture.

GUARDRAIL is a security framework specifically designed for the large language model (LLM) application ecosystem, providing comprehensive protection especially for the Model Context Protocol (MCP). It uses a multi - layer architecture, including an information gateway layer, a context verification layer, a request control layer, an execution isolation layer, and an audit monitoring layer, aiming to prevent data leakage, unauthorized access, and resource abuse. This framework emphasizes progressive adoption, allowing developers to gradually enhance security while maintaining high performance and ease of use.

SolidPilot is an open-source SolidWorks AI assistant that uses a modular architecture to interact with local language models (such as Claude) through the MCP protocol, including technical components like the Python layer, C# adapter layer, and COM bridge.

An implementation of the MCP server based on iFlytek, supporting intelligent workflow scheduling and multi - model hybrid application architecture.

The GLM-4.6 MCP Server is an enterprise-level architecture consulting protocol bridging service that connects Claude 4.5 Sonnet and GLM-4.6 through the model context protocol, providing system design, scalability patterns, and technical decision-making support. It supports functions such as code architecture analysis, system architecture design, and decision review.

MCP - ChatBot is a chatbot application based on the Modular Capability Protocol (MCP). It integrates weather services and the GPT - 4o model through a containerized architecture to provide natural language interaction capabilities.

Memory Bank MCP is a project knowledge management system based on the Model Context Protocol (MCP). It helps teams manage project knowledge through AI - generated and structured documentation, including six core document types such as project goals, product context, system architecture, technical details, current tasks, and progress.

Auto Causal Inference is a project that uses large language models (LLMs) to automatically perform causal inference. Users only need to specify the treatment variable and the outcome variable, and the system can automatically complete the full - process analysis, including variable role identification, causal graph construction, effect estimation, and model validation. The project provides two agent architectures (LangGraph and MCP) to achieve this function, which is particularly suitable for causal problem analysis in the banking scenario.

The YaraFlux MCP Server is a YARA scanning server based on the Model Context Protocol (MCP), providing file YARA rule analysis capabilities for AI assistants. This project uses a modular architecture, integrates 19 MCP tools, supports YARA rule management, file scanning, secure storage, etc., and can be seamlessly integrated with AI assistants such as Claude Desktop.

Crawl4AI is an intelligent web crawler server based on the MCP architecture. It combines the Claude AI model to provide website content analysis and processing functions, supports customizable crawling depth and AI processing tasks, and is suitable for various scenarios such as research, content creation, and data analysis.

Createve.AI Nexus is an open - source project that bridges the gap between AI agents and enterprise systems by implementing the Model Context Protocol (MCP) standard. It provides secure, real - time data access and system integration capabilities. It supports multiple AI platforms, has an enterprise - level security architecture, and can connect various business applications, sensor data, and document management systems.

This project is part of the Model Context Protocol (MCP) ecosystem. It provides an MCP server that interacts with the Status Invest API for querying stock market data, such as stock prices, indicators, and payment dates. The project adopts a layered architecture design, including the domain layer, infrastructure layer, application layer, and interface layer, and supports data validation and external API integration.

This project demonstrates the implementation of a microservice architecture based on the NestJS Model Context Protocol (MCP), including an MCP server that provides time context functions and a client that integrates LangChain.js, supporting multiple server connections.

The MCP server based on the GLM-4.5V model provides intelligent image analysis functions, supporting image acquisition from file paths or the clipboard. It is specifically designed for code content extraction, architecture analysis, error detection, and documentation generation.

This project designs and implements an architecture that connects the data space with generative AI. Through the Model Context Protocol (MCP) server as a secure middle layer, the language model can indirectly access the DuckDB database. It has the characteristics of modularity, extensibility, and log tracking, laying the foundation for future upgrades to the RAG architecture.

Fluent MCP is a modern framework for building Model Context Protocol (MCP) servers with intelligent inference capabilities. It supports AI integration, tool separation, and offloading of complex inferences, and uses a dual - layer LLM architecture for efficient inference.

MCP Servers is a collection of servers and services for a Model Composition Platform (MCP), aiming to promote the integration and deployment of various AI/ML models and services. The project adopts a modular architecture, supporting standardized communication and scalable design, and includes various server types such as weather services.

A server - side implementation based on the Model Context Protocol (MCP), providing various tools required by AI agents and LLMs, including SearXNG - driven search functions and file system operations. Supports two implementation methods: It is recommended to use the FastMCP decorator to simplify development, or use the original architecture to implement more complex functions.

An AI code audit server based on the local Ollama model. Through the integration of the Model Context Protocol (MCP), it provides multi - dimensional code analysis, including security, integrity, performance, quality, architecture, testing, and documentation checks.

MockLoop MCP is the world's first AI-native API testing platform integrating the Model Context Protocol (MCP). It revolutionizes API testing through AI-driven scenario generation, automated test execution, and intelligent analysis capabilities. The platform offers 5 AI prompts, 15 scenario resources, 16 testing tools, and 10 context tools, supports a dual-port architecture and stateful testing workflows, and is suitable for enterprise-level compliance and advanced analysis requirements.

The NSAF MCP Server is a server-side implementation of the Model Context Protocol (MCP) used to connect AI assistants with the Neural-Symbolic Autonomous Framework (NSAF), providing evolutionary algorithms and architecture comparison functions.

The PyGithub MCP Server is a model context protocol server that interacts with the GitHub API through PyGithub. It provides a modular tool architecture, supporting complete GitHub issue management, intelligent parameter handling, and robust implementation.

The Memory MCP Server is a server that provides long-term memory functions for large language models (LLMs). By implementing the Model Context Protocol (MCP), it enhances the context awareness ability of LLMs in continuous interactions. The project provides a user-friendly API, supports storing and retrieving context information, is scalable, and is suitable for various LLM architectures.

This project builds a data statistical analysis Agent based on Python. Through the MCP server and client architecture, combined with the LangGraph Agent, it realizes data statistics, visualization, and modeling functions.

MCP Server Tools is an innovative API project based on the Model Context Protocol (MCP). It combines traditional API endpoints with intelligent model communication, adopts a domain - driven design (DDD) architecture, and provides a secure and extensible API experience.