Browse and discover Model Context Protocol compatible clients.
Build a local MCP server that turns files, images, and video into fast spatial search for AI agents using Gemini embeddings and ChromaDB
Debug AI agents in real time with Chrome DevTools to pause, inspect, and step through execution for faster issue resolution and control.
🗄️ Store and manage local JSON data for context engineering with GitHub Copilot and MCP clients efficiently.
Embed Google AI Studio in a desktop app and enable generated apps to interact securely with your local system through a JavaScript API.
Store persistent project memory for AI agents and MCP clients so they can resume work with context, decisions, and tasks intact
Explore 30+ AI agent frameworks to learn patterns, architectures, and source-backed implementation details for building better agents
Provide reliable, repository-scoped memory for AI coding agents with code citations, just-in-time verification, and stale-proof data management.
MCP servers and integrations for Vaquill - CourtListener, CanLII, chatbots, Slack, Discord, WordPress bots, and more.
MCP client for use by picoclaw and other crustaceans
Aggregate multiple Ollama instances into a unified OpenAI-compatible API with load balancing, health checks, auto discovery, and web management.
Route semantic tool requests through one MCP router to simplify AI access to 1,000+ tools in n2-qln
HTTP client, CLI, and MCP for modular, git-tracked request sequencing
Structure AI-assisted TDD with a plan-apply-unify loop for clearer tests, tighter context, and steady code progress
Control GitHub Actions workflows from your AI assistant to list runs, read logs, rerun jobs, cancel builds, and trigger deployments within your editor.
OmniXy is a universal Model Context Protocol client in Python, enabling seamless integration with any LLM. It supports structured reasoning, robust prompt management, and adheres to the MCP specification for consistency.