mcp-agent-reliability-scorer
Scores AI agent trajectories and detects silent failures, loops, and reliability issues with zero external API cost.
README
mcp-agent-reliability
Pure-computation MCP server that scores AI agent trajectories, detects silent failures, loops, and reliability metrics — zero external API cost.
What problem does this solve?
AI agents often fail silently: they loop on the same tool, return empty results, or give high-confidence answers without evidence. Debugging is expensive. This MCP gives you instant scores and failure reports from any agent run log you provide.
Think of it like a doctor’s check-up for your AI agent — it looks at the “X-ray” (the run log) and tells you what is healthy and what is broken, without needing another expensive doctor (LLM).
Tools
| Tool | Description |
|---|---|
score_trajectory_tool |
0-100 reliability score + breakdown |
detect_failure_modes_tool |
List of loops, empty results, high-confidence-without-evidence, etc. |
analyze_tool_usage_tool |
Per-tool call counts, error rates |
compute_success_rate_tool |
Success rate across many runs |
compare_trajectories_tool |
Which of two runs is more reliable |
Quick Start (local)
# Install
pip install -e .
# Run (HTTP on port 8080)
python -m mcp_agent_reliability.server
Or with MCP inspector / Claude Desktop / Cursor by pointing to the HTTP endpoint.
Example trajectory input
[
{"tool": "get_weather", "status": "ok", "result": {"temp": 28}},
{"role": "assistant", "content": "28C today", "is_final": true}
]
Why this is valuable for entrepreneurs
- Zero running cost (no paid APIs)
- Helps you ship reliable agents faster → happier users → more revenue
- Can be called by the agent itself mid-run or by your CI after tests
- Fits the “Type A” high-margin MCP pattern preferred on MCPize
Development
pytest
License
MIT
Built daily for Prince Ruhul / Prevalid by the Daily AI Project Builder.
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