CICADA is an MCP server that provides structured code indexing for AI code assistants. Through AST-level indexing, call site tracking, and semantic search, it provides efficient context compression for Elixir, Python, and Erlang code repositories, reducing token usage and improving the quality of code understanding.

Kontxt MCP Server is a codebase context analysis service based on the Gemini model, providing codebase understanding tools for AI clients and supporting multiple transport protocols and context attachment functions.

NetContextServer is a tool that enhances AI programming assistants' understanding of .NET codebases. It provides functions such as in-depth code analysis, semantic search, and test coverage analysis through the Model Context Protocol (MCP).

Code Graph RAG MCP is an advanced model context protocol server that provides 13 professional tools for code understanding, relationship mapping, and semantic search through intelligent graph representation and multi - language code analysis capabilities.

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.

This project implements the Model Context Protocol (MCP) as a practical solution for the Personal Intelligence Framework (PIF). It builds a meaningful space for the development of understanding between humans and AI through structured tools and a progressive interaction mode.

MCP-Memos is a memo tool based on the MCP protocol, designed specifically for developers. It supports quick recording and retrieval of text information without switching applications. It uses large language models to provide powerful fuzzy search capabilities, including semantic understanding, context awareness, and natural language query functions.

OmniMCP is a tool that provides rich UI context and interaction capabilities for AI models through the Model Context Protocol (MCP) and OmniParser. It focuses on achieving in - depth understanding of the user interface through visual analysis, structured planning, and precise interaction execution.

An MCP server project that enables Claude to directly access the documentation of popular libraries such as LangChain, LlamaIndex, and OpenAI, search through the Serper API and parse content using BeautifulSoup to enhance the AI's context understanding ability.

OmniMCP is a project that provides rich UI context and interaction capabilities for AI models through the Model Context Protocol (MCP) and OmniParser. It supports functions such as visual perception, LLM planning, and action execution, enabling in - depth understanding and precise interaction with the user interface.

The MCP project is a standardized protocol for enhancing the context understanding ability of AI models. It provides environmental information, user preferences, and conversation history in a structured manner, solving the problem of limited memory in AI systems. The project includes installation guides, environment configuration, and multiple experimental cases.

Nabu and Nisaba are research prototype toolkits used to enhance the code understanding and development efficiency of LLM agents. Nabu, as an MCP server, provides code semantic search and structure analysis functions, supporting multiple programming languages; Nisaba provides workspace management capabilities for Claude Code through proxy injection and a TUI interface, helping agents manage context usage autonomously.

MiniMe-MCP is an upgraded project for an AI development assistant. By creating a digital twin of the developer, it enables persistent memory and intelligent pattern recognition across projects. It solves the problem that traditional AI assistants lack memory and context understanding, provides personalized coding suggestions based on historical experience, supports multiple IDE tools, and can run locally to ensure data privacy.