CodeMap is a Roslyn-powered MCP server that lets AI agents navigate C# codebases by symbol, call graph, and architectural fact, instead of brute-force reading thousands of lines of source code. One tool call. Precise answer. No context flood.
A modular MCP server that provides tools for file operations, regex-based code searching, and structural analysis of functions and classes across multiple programming languages. It also includes AI-powered features for intelligently updating files according to architectural changes.
Turns AI assistants into full-stack software engineers with 36 tools for cognitive reasoning, code validation, project scaffolding, and AI/IDE configuration generation across 130+ programming languages, databases, and frameworks.
An experimental MCP server that enables AI assistants to interact with VS Code workspaces through file operations, code execution, and Git management. It also provides tools for Docker integration, project scaffolding, and secure command execution using project-specific configurations.
Connects AI tools directly to Codemend production error monitoring to list, analyze, and resolve software crashes. It enables users to retrieve AI-generated fixes and automatically open GitHub pull requests to address production issues.
Enables AI agents to write and execute Python code in an isolated sandbox that can orchestrate multiple MCP tool calls, reducing context window bloat and improving efficiency for complex workflows.
An MCP server that aggregates multiple upstream MCP tools into a single 'codemode' tool for unified orchestration and execution. It enables agents to run generated code in an isolated sandbox to interact with various connected services through a single interface.
Deterministic code navigation MCP server for Codex/CodeCLI that provides compact repo context using git-aware deterministic search tools without semantic search or embeddings.
全面解析MCP Server PostgresMCP Server的核心功能、安装配置和实用案例。作为顶级Model Context Protocol服务器，MCP Server Postgres能让AI助手访问实时数据、执行操作，为您提供更智能的工作体验和自动化解决方案。
Provides tools to search and execute Code Ocean capsules and pipelines while managing platform data assets. It enables users to interact with Code Ocean's computational resources and scientific workflows directly through natural language interfaces.
Enables automation of workflows across GitHub, Notion, and Google Calendar with AI-powered task execution, personalized agent memory, and Slack integration for seamless team coordination and project management.
Enables LLMs to retrieve, analyze, and programmatically implement CodeRabbit AI code review suggestions on GitHub pull requests, with automated workflows for processing and resolving review comments.
A high-performance MCP server providing lightning-fast hybrid code search using TF-IDF and vector embeddings for AI assistants. It enables real-time codebase indexing and semantic retrieval with sub-50ms latency and offline support.
AI modifies a function but misses 5 callers that also need updating. crag fixes this — it builds precise call graphs via static analysis, so AI knows exactly what's affected before making changes.
Code Reviewer AI - MCP server providing AI-powered tools and automation by MEOK AI Labs
Enables comprehensive GitHub PR reviews through Cursor's AI by fetching PR diffs, running static analysis tools (ESLint, Prettier, TypeScript, Semgrep), executing tests, and generating detailed code review reports with inline comments.
Enables AI agents to autonomously create and manage topic-specific vector knowledge bases with end-to-end functionality including project creation, content ingestion from URLs, semantic search, and progress tracking. Provides a complete research workflow without exposing low-level APIs.
An MCP server to create secure code sandbox environment for executing code within Docker containers. This MCP server provides AI applications with a safe and isolated environment for running code while maintaining security through containerization.
A secure Model Context Protocol server that allows AI assistants and LLM applications to safely execute Python and JavaScript code snippets in containerized environments.
Enables LLMs to perform high-performance code search and analysis across multiple languages using symbol indexing, regex text search, and structural AST pattern matching. It also provides tools for technology stack detection and dependency analysis with persistent caching for optimized performance.
