MCP Clients

Manage and debug local Model Context Protocol servers with an easy desktop app that auto-discovers and monitors MCP instances.

Generate configuration files for AI coding agents by detecting your stack and applying community rules for better session context.

Manage and coordinate multiple AI CLI instances with real-time monitoring, hierarchical control, and multi-agent verification for large-scale automation.

Deliver a local, Windows-based execution layer for MCP-capable agents with zero cloud dependency and broad agent support.

Enable runtime analysis and control of AI agents with a dynamic process hacking server similar to Process Hacker for enhanced debugging and integration.

Orchestrate multi-agent workflows with LangGraph for Python 3.11+, with a lightweight, tested framework for routing tasks and agents

Build and manage Claude agent workflows with a TypeScript and SvelteKit app for structured AI automation and local SQLite storage

Provide Twitter data access with user profiles, tweet search, follower events, and influencer tracking through an easy-to-deploy MCP server.

🔒 Access and manage encrypted SQLite databases effortlessly with this MCP server, designed for seamless integration with SQLCipher and various clients.

🚀 Streamline build and dev output by feeding logs directly into AI coding sessions using Model Context Protocol for seamless automation.

Extract and convert web pages into clean Markdown, preserving structure and media, with automatic mode selection and zero setup required.

Simple MCP server that returns "Hello World!" message. Easy to set up with any MCP-compatible AI client. 🌍💻

Provide a TypeScript client that reverse-engineers QClaw's WeChat OAuth2 login and jprx gateway protocol for seamless Tencent API access.

Structure AI-assisted TDD with a plan-apply-unify loop for clearer tests, tighter context, and steady code progress

Detect and reduce wasteful AI token usage by warning on large outputs, verbose logs, and repeated history in MCP-compatible clients.