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Documentation Index

Fetch the complete documentation index at: https://docs.multivon.ai/llms.txt

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multivon-mcp is a Model Context Protocol server that exposes 19 evaluation tools to any MCP-compatible agent — Claude Desktop, Claude Code, Cursor, Cline, OpenCode. The agent calls them by name; no copy-paste, no python -c "...", no asking the agent to figure out the SDK calls. When the agent is helping you build an LLM product, it can:
  • Score a RAG output for hallucination without you writing the scaffolding
  • Generate an adversarial PDF on demand to test your document AI
  • Run the full pdfhell mini-suite against a model and analyse the results
  • Produce a hash-chained audit pack for procurement diligence
  • Discover the full evaluation capability catalog as JSON

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Why an MCP server (not just a CLI)

The MCP layer matters because the agent is the user. A CLI is the right shape for humans driving terminals; an MCP server is the right shape for an LLM that needs to call the tool mid-edit, get JSON back, and condition its next action on the result. Three concrete wins:
  1. Tool discovery is free. eval_discover returns the full 44-evaluator catalog as JSON. The agent never has to parse markdown to know what’s available.
  2. JSON results, not pretty-printed. Every tool returns {"score": 0.0-1.0, "passed": bool, "reason": str, "threshold": float}. The agent can branch on the score programmatically.
  3. Calibration is automatic. Each evaluator uses the calibrated threshold for the configured judge — no asking the agent to guess at the right cutoff.

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Install

pip install multivon-mcp
The bare install pulls multivon-eval, pdfhell, and the MCP SDK. Provider SDKs (anthropic, openai, google-genai) come along — bring your own API key in env.
For one-off use without installing, uvx multivon-mcp works zero-setup.

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Next steps