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Coordinate exclusive resource claiming, releasing, and listing across multiple AI agents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-mcp-locks" yet — see the docs or source repo.
Use mcp-mcp-locks to claim available browser profile locks for three parallel AI tasks, list which resource each task gets, and then release all locks when the tasks finish.
A list of browser profiles successfully claimed by each task and the final release results, preventing resource collisions.
List the currently occupied and available exclusive resource locks in the system, and identify which locks may block new automation tasks from starting.
A current lock status report that helps determine resource usage and whether upcoming tasks can run safely.
After an automation agent exits unexpectedly, use mcp-mcp-locks to inspect and release its leftover resource locks, then confirm those resources are available again.
A list of cleaned-up stale locks and the post-release availability status, ensuring later agents can reuse the resources.
Turn any web API into a governed, auditable, agent-ready MCP server.
Aggregate multiple MCP servers into a few fixed tools for AI apps.
Aggregate multiple MCP servers into one for search, parallel calls, and orchestration.
Publish, author, and reuse AI tools and skills via MCP and REST.
Control CLI processes through MCP for execution, interaction, and output retrieval.
Secure LLM and MCP tool interactions with zero-trust controls and policy enforcement.