mcp-use
Reduces time-to-market for AI-integrated applications by providing a unified framework for building both client-side MCP apps and server-side agents, elimina…
The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.
- Build custom Claude integrations that connect to your existing APIs and databases
- Create MCP servers that let AI agents autonomously execute code and system tasks
- Develop fullstack applications that combine Claude's reasoning with backend automation
Reduces time-to-market for AI-integrated applications by providing a unified framework for building both client-side MCP apps and server-side agents, eliminating fragmented tooling and integration overhead.
Full-stack engineering teams building Claude/ChatGPT integrations and AI agent infrastructure at scale.
https://github.com/mcp-use/mcp-use
By mcp-use
How to Get It
claude mcp add mcp-use -- npx -y mcp-use
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
Once it’s connected, paste this into Claude:
Help me build custom Claude integrations that connect to my existing APIs and databases
Trust Signals Auto-scanned
Data & Access
Community Pulse New
- Show HN: Mcp-use – Connect any LLM to any MCP — Hacker News · 155 pts
- Show HN: We built an open source BYOK CLI that supports any model and any MCP — Hacker News · 6 pts
- Show HN: OSS Python client for creating MCP capable agents in 6 lines of code — Hacker News · 5 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 10,342 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 10,342 GitHub stars; contributors unknown; last commit 0d ago; license MIT.