An MCP server that indexes Python codebase structures to help AI assistants discover and reuse existing functions instead of duplicating code. It enables real-time searching of function metadata, duplicate detection, and structural analysis across multiple projects.
Generates narrated video walkthroughs of git commits, staged/unstaged changes, or entire codebases with AI-powered analysis, syntax-highlighted code visualization, and text-to-speech narration.
An observational memory server that uses LLM agents to capture, compress, and recall project-specific decisions and context across AI coding sessions. It stores structured observations in SQLite to maintain long-term session continuity and architectural awareness.
Integrates with Google CodeWiki to search, fetch, and cache documentation for GitHub repositories, enabling easy access to repository documentation through natural language queries.
Local MCP server that wraps the codex CLI asynchronously, returning a job_id immediately to avoid MCP protocol timeouts, and providing tools to start, poll, list, and cancel codex jobs.
An MCP server for the OpenAI Codex CLI that provides coding assistance with multi-turn session management and reasoning depth control. It enables users to perform code analysis, generation, and refactoring through Claude with native resume support for conversational context.
Enables remote execution of Codex CLI commands and provides an MCP tool for AI agents to escalate questions to humans via Telegram, allowing for human-in-the-loop workflows when away from the machine.
An MCP server that wraps the OpenAI Codex SDK to deploy multiple specialized AI agents with individual configurations for models, sandboxing, and behavior. It enables users to manage dedicated tools for tasks like code review and test writing through a customizable agent factory.
Enables peer discovery and direct messaging between multiple Codex sessions running on a single machine. It allows AI sessions to coordinate, find other active peers by repository or context, and exchange messages via a local broker.
公开目录未提供摘要。
全面解析Coding Project StructureMCP 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，Coding Project Structure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。
A powerful and flexible Node.js Express server that provides a comprehensive interface to the CoinGecko cryptocurrency data API. This server supports both the free public API and the Pro API with automatic fallback.
Provides access to CoinGlass cryptocurrency derivatives data, including funding rates, open interest, and liquidation metrics. It enables LLMs to analyze real-time and historical market structure context through a standardized toolset.
Provides access to cryptocurrency market data, exchange information, and blockchain metrics through the CoinMarketCap API. Supports price quotes, historical data, trending tokens, global metrics, and DEX information across different subscription tiers.
Enables AI coding assistants like Claude Code, Cursor, and Codex to share chat logs, terminal history, and session context with each other. Eliminates the need to re-explain context when switching between different AI coding tools.
A Model Context Protocol server that enables users to control and interact with Google Colab instances via a reverse proxy. It supports executing shell commands, running persistent Python code, and monitoring system resources like CPU, GPU, and RAM.
An MCP server that bridges local AI agents to Google Colab notebooks, enabling execution of Python code and interaction with notebook states directly from the agent.
Enables searching over 5,400 Solana hackathon projects and 84,000 crypto archive documents to validate startup ideas and research the crypto ecosystem. It provides tools for cohort analysis, project deep-dives, and access to historical cypherpunk literature and protocol documentation.
Enables AI video automation pipeline: ComfyUI image-to-video, FFmpeg processing, After Effects template rendering, review, and multi-platform publishing preparation.
Enables Claude to generate images through a local ComfyUI instance using Stable Diffusion and SDXL models via natural language. Users can trigger image generation, list available checkpoint models, and monitor the ComfyUI queue status directly from their MCP client.
