A
license-
qualityC
maintenanceEnables LLM agents to perform SRE reliability calculations like error budgets and burn rates using deterministic tools, integrating with Prometheus and Loki for real data.
MIT
Agent Reliability MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Agent Reliability MCP ServerScore agent reliability from 100 runs: 85 succeeded, 15 failed"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Agent Reliability MCP Server
Compute AI agent reliability scores, success rates, latency stats and failure patterns — pure math, zero API cost.
This MCP server gives any AI agent (Claude, Cursor, ChatGPT, etc.) the ability to analyze how reliable other AI agents are using only the numbers you feed it. Perfect for entrepreneurs building agent products who want quick, trustworthy metrics without expensive observability platforms.
Why this exists
When you ship an AI agent, it sometimes fails. Counting successes by hand is boring. This server does the hard math for you in one tool call.
Related MCP server: MCP Agent Reliability Server
Tools
Tool | What it does |
| Gives an overall 0-100 reliability score + letter grade |
| Success % with statistical confidence interval |
| Mean, median, p95, p99 latency numbers |
| Finds the most common error messages |
| Monte-Carlo projection of future success |
| Which of two agents is more reliable? |
Quick start (after publish)
npx @mcpize/cli install agent-reliability-mcpOr add to your MCP client config.
For entrepreneurs
Zero running cost (no external APIs)
Helps you decide which agent version to ship
Works offline
Ready for MCPize marketplace
License
MIT
This server cannot be installed
Maintenance
–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity
Related MCP Servers
- Alicense-qualityCmaintenanceLogs tool call results, calculates reliability scores, and generates reports for AI agents, helping entrepreneurs measure agent reliability without paid APIs.MIT
- Alicense-qualityBmaintenanceMCP server that analyzes AI agent execution logs to calculate reliability scores, detect failure patterns, and suggest concrete improvements for making AI agents more reliable.MIT
- Alicense-qualityCmaintenanceAnalyzes multi-step AI agent tool chains to compute success probability, identify bottlenecks, and suggest better execution orders, enabling more reliable agents via local pure-math computation.MIT
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