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Verify, score, route, compare, and delegate AI agent decisions.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.Garl-Protocol/mcp-server" MCP server from askskill: Run: claude mcp add 'io-github-garl-protocol-mcp-server' -- npx -y @garl-protocol/mcp-server
Use this MCP tool to evaluate multiple action options, compare trust, risk, and fit, and recommend a route.
A comparison of options with trust scores, risk notes, and a recommended path.
Check whether this AI agent’s conclusion is reliable, and provide verification results, confidence scores, and items needing human review.
Verification result, score, and key points that need review.
Based on the task conditions, decide which sub-agent or workflow should handle it and explain why.
A delegation recommendation, matching rationale, and next steps.
When multiple AI agents produce conflicting answers, teams can compare, score, and choose the most trustworthy result. It fits workflows that need lower decision risk.
Before automated execution, users can validate and route decisions to avoid passing unreliable conclusions to the next step.
When tasks need to be assigned to different agents or workflows, it can help with delegation and comparison. Teams can decide faster who should handle what.
It is built for AI-agent scenarios and provides trust-related capabilities such as verification, scoring, routing, comparison, and delegation. It helps users choose and distribute agent tasks more safely.
It is best suited for developers, researchers, and product managers who need to manage AI agent outputs. It also fits anyone concerned with the reliability of automated decisions.
The provided information only says it is an MCP server and does not list extra installation or key requirements. For exact setup details, see the source repository.
Provide tamper-evident receipts, trust scoring, and capability tokens for AI agents.
Add a fail-closed verification gate before AI agents take action.
Delegate and coordinate multi-agent tasks to automate complex workflows efficiently.
Coordinate AI agents through negotiation for more efficient automated workflows.
Verify AI agents and MCP servers, then issue verifiable competency credentials.
Test API compatibility for AI agents with scores, grades, and recommendations.