Browse and discover Model Context Protocol compatible clients.
Arcade Python SDK, CLI, and toolkits
CLI MCP package manager & registry for all platforms and all clients. Search & configure MCP servers. Advanced Router & Profile features.
AgentKit enables building multi-agent networks with deterministic routing and rich tooling via MCP, supporting TypeScript AI developers with fault-tolerant cloud deployment. It offers flexible routing, multiple model providers, and built-in tracing.
📄 The PDF intelligence layer for AI agents — Agent Document Twin, evidence-first extraction, visual crops, OCR provenance, trust reports, and benchmark-gated releases. MCP server for Claude, Cursor, VS Code, and any MCP client.
A secure local sandbox to run LLM-generated code using Apple containers
Simple, modular, and observable Go framework for backend applications.
Production-Ready MCP Server Framework • Build, deploy & scale secure AI agent infrastructure • Includes Auth, Observability, Debugger, Telemetry & Runtime • Run real-world MCPs powering AI Agents
A minimalistic MCP client with a good feature set.
Agent samples built using the Strands Agents SDK.
Ollama MCP client: TUI for managing local LLMs. Multi-server, streaming, tool management, & full model config. Dev-focused.
**Concise Descriptions (under 200 characters):** * **OpenMCP client: VS Code extension for MCP development. Streamlines workflow & enhances developer experience.** (100 characters) * **VS Code plugin
A universal CLI client for MCP. mcpc supports persistent sessions, stdio/HTTP, OAuth 2.1, tasks, JSON output for code mode, proxy for AI sandboxes, x402, and more.
One memory layer, every AI tool. Store anything once — recall it in Claude, ChatGPT, Cursor, or any MCP client. Self-hosted on Cloudflare's free tier.
LangGraph-powered ReAct agent with Model Context Protocol (MCP) integration. A Streamlit web interface for dynamically configuring, deploying, and interacting with AI agents capable of accessing vario
**Option 1 (Focus on LLM client):** LLM desktop client leveraging MCP for easy access & use of Large Language Models. **Option 2 (Focus on MCP integration):** MCP-integrated desktop client simplify