LLM Context is a tool that helps developers quickly inject code/text project content into the chat interface of large language models, supporting intelligent file selection and multiple integration methods.

MCP2Lambda is an MCP protocol server that runs AWS Lambda functions as tools for large language models (LLMs). AI models can call Lambda functions to access private resources and AWS services without code modification.

MCP2Lambda is an MCP server that allows AWS Lambda functions to be used as tools for large language models (LLMs) without modifying the code. It builds a bridge between AI models and Lambda functions through the Model Context Protocol (MCP), enabling models to access private resources and perform custom operations.

This project is a Model Context Protocol (MCP) adapter used to connect large language models (LLMs) with the Lisp development environment, supporting interaction through the lightweight Lisply protocol. The main functions include Lisp code evaluation, HTTP requests, and debugging support, suitable for scenarios such as AI-assisted symbolic programming and CAD design automation.

The MCP service for Ant Design components provides support for component documentation query and code generation for large language models

This project is a document processing server based on the Model Context Protocol (MCP) standard. By building a vector database and an MCP interface, it enables AI assistants to access external document resources and break through the knowledge limitations of large language models. The project includes two major components: a document processing pipeline and an MCP server. It supports multiple embedding models and file formats and can be applied to scenarios such as querying the latest technical documents and understanding private code libraries.

An MCP server that provides file system context for large language models, supporting file operations, code analysis, and intelligent search.

apktool-mcp-server is an MCP server based on Apktool, integrating large language models (such as Claude), providing real-time reverse engineering support, including vulnerability analysis, manifest parsing, and code review.

FastApply MCP Server is an enterprise-level code intelligence platform that provides comprehensive code analysis, search, and refactoring capabilities through local AI models, AST semantic search, security scanning, and intelligent pattern recognition. It supports multi-language development and large codebase processing.

LSP - MCP is a bridge server that connects the Language Server Protocol (LSP) and the Model Context Protocol (MCP), allowing large language models to query LSP functions such as code hover information and completion suggestions through a standard interface.

A code modification and generation service based on large language models

MCP-JaCoCo is a service tool that converts JaCoCo code coverage reports into a format suitable for large language models (LLMs), making AI-driven analysis more efficient

Container-MCP is a container - based secure tool execution platform that provides an isolated environment for large language models to run code, commands, and perform network operations, implementing a secure implementation of the MCP protocol.

Code Merge MCP is a code processing server based on the MCP protocol, providing code file extraction, merging, and analysis functions to help large language models efficiently process codebases.

This project implements a server based on the MCP protocol for querying the French legal database via the Legifrance API, supporting integration with large language models such as Claude and providing search functions for legal provisions, codes, and precedents.

An MCP protocol server implementation based on Pyodide, providing Python code execution capabilities for large language models

Checkstyle MCP Server is a middleware that connects large language models with local code quality tools, supporting code checking and formatting for languages such as Go, Java, and Lua, and providing project configuration management and AI self - correction functions.

The MCP Code Indexer is an intelligent code retrieval tool designed specifically for large AI language models. It improves the efficiency and accuracy of code processing through semantic understanding and vectorized indexing, supporting functions such as code analysis, quality assessment, and dependency management.

The Sandbox Fusion MCP Server is an implementation that provides code interpreter functionality for large language models. It supports code execution in multiple programming languages and Jupyter notebook operations, and communicates with clients through stdio interaction.

CTX is a tool that connects code repositories with large language models (LLMs). By automatically generating structured context files, it solves the problem of low efficiency in code context transfer in AI development. It supports collecting information from various sources such as code files, Git repositories, and web pages, and can directly provide project context for AI assistants through the MCP server.

This is a prototype of a code execution server based on the MCP protocol, which combines the code generation capabilities of large language models with MCP tool integration, enabling AI agents to run Python code in an isolated sandbox and invoke external tools.

A command-line tool that converts OpenAPI specifications into MCP server code, bridging large language models and APIs.

A code review server based on the MCP protocol that uses Repomix and large language models to perform structured analysis of code and provide review suggestions.

The 302AI Basic MCP Server is a toolkit that enhances the basic capabilities of large language models. It supports dynamically loading a list of remote tools, provides multiple usage modes (local stdin or remote HTTP service), and includes practical tools such as Wiki search, Arxiv search, calculator, and code execution.

ghcontext is a tool that connects GitHub with large language models. It provides real-time repository information, including API documentation, README content, and code structure, for AI assistants through the Model Context Protocol (MCP), ensuring that the information obtained by AI is always up-to-date.

A server that provides file system context for large language models, supporting file operations, code analysis, and intelligent caching.

A custom code review server based on the MCP protocol that uses Repomix and large language models for code analysis and provides structured review reports.

Code Index MCP is a Model Context Protocol server that helps large language models index, search, and analyze code in project directories. It supports multiple programming languages and persistent storage.

The Figma MCP Server is an innovative middleware that seamlessly integrates Figma design resources with large language models (such as Claude, Windsurf, etc.) through the Model Context Protocol (MCP) to achieve automated conversion from design to code.

MCP server development guide, introducing how to build a standardized communication protocol server for large language models (LLMs), including core architecture, function implementation, and example code.

An MCP server tool for automatically dumping codebase content into a format suitable for processing by large language models, supporting recursive scanning, ignoring.gitignore rules, skipping binary files, and chunking large codebases.

Code2Prompt is a tool for converting codebases into structured prompts, supporting automatic code processing, intelligent filtering, template customization, token tracking, and Git integration, facilitating interaction with large language models.

VectorCode is a code repository indexing tool designed to optimize the prompt construction of large programming language models (LLMs) by indexing and providing code repository information. It supports multiple embedding engines, provides command - line tools and Neovim plugins to help developers more efficiently use project context to improve the quality of model output.