pluggedin-app
Centralizes MCP server discovery and configuration across fragmented AI tooling, reducing integration overhead and operational visibility gaps for teams runn…
The Crossroads for AI Data Exchanges. A unified, self-hostable web interface for discovering, configuring, and managing Model Context Protocol (MCP) servers—bringing together AI tools, workspaces, prompts, and logs from multiple MCP sources (Claude, Cursor, etc.) under one roof.
- Ask Claude to find all available MCP servers across your connected tools and workspaces.
- Generate a report of which AI data sources are currently active and configured in your system.
- Automate discovery and management of Model Context Protocol integrations from multiple platforms.
Centralizes MCP server discovery and configuration across fragmented AI tooling, reducing integration overhead and operational visibility gaps for teams running multiple AI workspaces.
Engineering teams managing multiple MCP-based AI tools seeking unified server discovery and configuration without vendor lock-in.
https://github.com/VeriTeknik/pluggedin-app
By VeriTeknik
How to Get It
claude plugins install VeriTeknik/pluggedin-app
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.
After installing, paste this into Claude:
Find all available MCP servers across my connected tools and workspaces
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 97 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): 97 GitHub stars; contributors unknown; last commit 0d ago; license MIT.