multivon-mcp 0.2.1

MCP server exposing multivon-eval + pdfhell as agent-callable tools. Drop into Claude Desktop, Cursor, Cline, or any MCP-compatible AI coding agent.

multivon-mcp

MCP server that gives AI coding agents direct access to evaluation tools. Drop into Claude Desktop, Claude Code, Cursor, Cline, or any Model Context Protocol–compatible agent.

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

No copy-paste, no python -c "...", no asking the agent to figure out the SDK calls.

Install

pip install multivon-mcp

Bare install pulls multivon-eval, pdfhell, and the MCP SDK. The provider SDKs (anthropic, openai, google-genai) come along too — bring your own API key in env.

Configure your agent

Claude Desktop / Claude Code

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "multivon": {
      "command": "multivon-mcp",
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "OPENAI_API_KEY": "sk-proj-...",
        "GOOGLE_API_KEY": "AIza..."
      }
    }
  }
}

Restart Claude. The 19 tools become available; ask Claude "use multivon to evaluate this RAG output" and it figures out which tool to call.

Cursor

cursor.json or via Settings → MCP:

{ "mcpServers": { "multivon": { "command": "multivon-mcp" } } }

Cline / OpenCode / any MCP-compatible agent

Same shape — point at the multivon-mcp console script.

Local dev / debugging

mcp dev multivon_mcp.server

Opens the MCP Inspector UI in your browser. You can call any tool by name, see the JSON schemas, and watch the requests/responses.

The 19 tools

Tool	What it does	API key needed
eval_discover	Returns the full machine-readable capability catalog (evaluators, traps, suites, calibration data, versions). Call this first.	No
pdfhell_make	Generates one adversarial PDF + its answer key. Useful for inspecting what a trap looks like.	No
pdfhell_run	Runs the pdfhell adversarial-PDF benchmark against a vision model. Returns pass rate, per-trap CIs, suite hash.	Yes (vision provider)
eval_faithfulness	QAG-graded faithfulness — is a RAG output grounded in the retrieved context?	Yes (judge)
eval_hallucination	QAG-graded hallucination detection — does an output contain content NOT in context?	Yes (judge)
eval_relevance	QAG-graded answer-vs-question relevance.	Yes (judge)
eval_answer_accuracy	QAG-graded semantic equivalence vs ground truth.	Yes (judge)
eval_tool_call_accuracy	Deterministic agent tool-call correctness. No LLM.	No
eval_audit_pack	Build a hash-chained, procurement-ready ZIP from a pdfhell run.	No
eval_pii_detection	Local-only regex scan for PII (GDPR / CCPA / PIPEDA / HIPAA jurisdiction packs). No API calls.	No
eval_schema_compliance	Validate an LLM output against a JSON Schema; reports per-field errors, not just valid/invalid.	No
eval_toxicity	QAG-graded toxicity / harmful-content detection.	Yes (judge)
eval_bias	QAG-graded bias detection across gender, race, politics, age, socioeconomic axes.	Yes (judge)
eval_context_precision	RAG retrieval quality — are the retrieved chunks on-topic?	Yes (judge)
eval_context_recall	RAG retrieval completeness — does retrieved context contain enough info to answer?	Yes (judge)
eval_g_eval	G-Eval style holistic 0.0-1.0 scoring against a plain-English criterion.	Yes (judge)
eval_custom_rubric	Score an output against your own list of yes/no quality checks.	Yes (judge)
eval_vqa_faithfulness	Image-grounded visual-QA faithfulness — does the answer match what's in the image?	Yes (vision judge)
eval_document_grounding	Multi-page document-grounded faithfulness for document-AI agents.	Yes (vision judge)

Example session

User: I just shipped a RAG endpoint. Can you check it for hallucinations?

Claude: I'll use multivon to evaluate it.
        [calls eval_discover to see what's available]
        [calls eval_faithfulness with your input/context/output]

→ score: 0.667 (passed: False), threshold: 0.9
  reason: 2/3 claims grounded
    ✓ "annual renewal" — supported by context
    ✓ "30-day notice" — supported by context
    ✗ "automatic upgrade" — NOT in context

Claude: Your RAG hallucinated the "automatic upgrade" detail. The context
        doesn't mention upgrades. I'd add a Hallucination evaluator to your CI
        gate, threshold ≥0.85, and re-prompt with explicit "only use facts
        from context" instructions.

Why these 19 tools (not all 44)

eval_discover returns the full 44-evaluator catalog, so the agent can always introspect everything. The 19 tools we expose directly are the ones agents actually call mid-edit:

• RAG generation checks (faithfulness, hallucination, relevance, answer_accuracy)
• RAG retrieval checks (context_precision, context_recall)
• Safety / fairness guardrails (toxicity, bias)
• Compliance (pii_detection, schema_compliance) — local-only, no API egress
• Flexible scoring (g_eval, custom_rubric) for user-defined rubrics
• Multimodal (vqa_faithfulness, document_grounding) for vision agents
• Agent traces (tool_call_accuracy)
• Document AI (pdfhell.run, pdfhell.make) — for any RAG-on-PDFs flow
• Audit pack — when procurement is involved
• Discover — meta-capability for planning

Exposing all 44 evaluators as MCP tools would bloat the agent's context window and overwhelm tool-selection. If you need an evaluator that's not directly exposed, the agent can still use multivon-eval as a library — eval_discover returns the import paths.

Dependencies

• mcp[cli] >= 1.0 — official MCP Python SDK + the mcp dev inspector
• multivon-eval >= 0.7.3 — the evaluator surface this wraps
• pdfhell >= 0.1.0 — the adversarial-PDF benchmark this wraps

All Apache 2.0.

License

Apache 2.0.

Citing

@software{multivon_mcp,
  title  = {multivon-mcp: MCP server exposing multivon-eval + pdfhell as agent-callable tools},
  author = {Multivon},
  year   = {2026},
  url    = {https://github.com/multivon-ai/multivon-mcp},
}