mcp-ai-accountability

Make your AI agents trustworthy and accountable.

This is a production-ready Model Context Protocol (MCP) server that helps AI agents (and the entrepreneurs who build them) measure, track, score, and improve reliability.

Why this exists

AI agents fail silently every day.
Tool calls time out. Schemas drift. Hallucinations happen.
Entrepreneurs lose money and trust because they cannot see why their agent is unreliable.

This MCP server turns reliability into a first-class tool that any agent can call.

Related MCP server: runmeter

What it does (simple language)

Imagine your AI agent is a delivery boy.
Sometimes he delivers the package late or to the wrong house.
This server is like a scorecard + GPS tracker + coach that:

• Records every delivery (tool call)

• Gives a reliability score (0-100)

• Tells you why things went wrong

• Suggests how to make the agent better next time

Perfect for founders who want accountable AI products.

Main Tools

Quick Start

# Install
pip install mcp-ai-accountability

# Or run from source
uv sync
uv run python -m src.server

Add to your Claude / Cursor / any MCP client:

{
  "mcpServers": {
    "ai-accountability": {
      "command": "uv",
      "args": ["run", "python", "-m", "src.server"],
      "env": {
        "RELIABILITY_DB": "./reliability.db"
      }
    }
  }
}

For Entrepreneurs (non-developers)

You do not need to write code.
Just connect this MCP to your existing AI agent (Claude Desktop, Cursor, etc.).
Then ask your agent:

"Check my reliability score for the last 24 hours and tell me the top 3 problems."

The agent will use the tools automatically and give you a clear report.

Pricing Suggestion

• Free tier: 1,000 interactions / month

• Pro: $29/month – unlimited + audit reports + Slack alerts

• Enterprise: custom (SSO + private hosting)

License

MIT

Built daily by the Prevalid team for the AI agent ecosystem.