Multivon

PyPI Python License Downloads

Docs · Website · PyPI · multivon-eval (engine)

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 22 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 22 tools

Discovery & document AI

Tool	What it does	API key
eval_discover	Full machine-readable capability catalog (evaluators, traps, suites, calibration data, versions). Call first.	No
pdfhell_make	Generate one adversarial PDF + its answer key.	No
pdfhell_run	Run the pdfhell adversarial-PDF benchmark against a vision model. Returns pass rate, per-trap CIs, suite hash.	Yes (vision)
eval_audit_pack	Build a hash-chained, procurement-ready ZIP from a pdfhell run.	No

RAG generation & retrieval

Tool	What it does	API key
eval_faithfulness	QAG-graded faithfulness — is a RAG output grounded in the retrieved context?	Yes
eval_hallucination	QAG-graded hallucination — does the output contain content NOT in context?	Yes
eval_relevance	QAG-graded answer-vs-question relevance.	Yes
eval_answer_accuracy	QAG-graded semantic equivalence vs ground truth.	Yes
eval_context_precision	RAG retrieval quality — are the retrieved chunks on-topic?	Yes
eval_context_recall	RAG retrieval completeness — does context contain enough info to answer?	Yes

Safety, compliance, fairness

Tool	What it does	API key
eval_toxicity	QAG-graded toxicity / harmful-content detection.	Yes
eval_bias	QAG-graded bias across gender, race, politics, age, socioeconomic axes.	Yes
eval_pii_detection	Local-only regex scan for PII (GDPR / CCPA / PIPEDA / HIPAA packs).	No
eval_schema_compliance	Validate an LLM output against a JSON Schema.	No

Agent & multimodal

Tool	What it does	API key
eval_tool_call_accuracy	Deterministic agent tool-call correctness. No LLM.	No
eval_vqa_faithfulness	Image-grounded visual-QA faithfulness.	Yes (vision)
eval_document_grounding	Multi-page document-grounded faithfulness for document-AI agents.	Yes (vision)

Flexible scoring

Tool	What it does	API key
eval_g_eval	G-Eval holistic 0.0-1.0 scoring against a plain-English criterion.	Yes
eval_custom_rubric	Score against your own list of yes/no quality checks.	Yes

Agent workflows (new in 0.3.0)

Tool	What it does	API key
eval_compare_runs	Diff two eval report JSONs — pass-rate delta, per-case regressions/improvements, McNemar p-value. Use after every fix to confirm it actually helped.	No
eval_generate_cases	Generate N eval cases (input / expected_output / context) from a chunk of source text. Eliminates the cold-start when building a new suite.	Yes (judge)
eval_ingest_trace	Convert a JSON agent trace (LangGraph / OpenAI Agents / manual) into an EvalCase payload. Use to score trajectories your agent just executed.	No

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 22 tools (not all 44)

eval_discover returns the full 44-evaluator catalog, so the agent can always introspect everything. The 22 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
• Agent workflows (compare_runs, generate_cases, ingest_trace) — the loop that turns one-shot scoring into iterative improvement

The three new 0.3.0 tools matter because evals are most useful as a loop, not a single call: generate a starting suite from your own docs (eval_generate_cases), run your agent over it, score the trace (eval_ingest_trace → eval_*), make a fix, then verify the fix improved things vs. the baseline (eval_compare_runs). Agents need that whole loop callable from within a conversation — otherwise they fall back to ad-hoc judgment.

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.

The Multivon ecosystem

Five public + one early-access package, all built on a shared evaluation engine:

Repo	What it is
multivon-eval	Python SDK — 44 evaluators + bootstrap CLI + multivon_eval.auto. The engine multivon-mcp wraps.
pdfhell	Adversarial PDFs that break AI document readers — exposed here as pdfhell_run + pdfhell_make tools
multivon-mcp (you are here)	MCP server — 22 tools from multivon-eval + pdfhell
eval-action	GitHub Action — runs the same evals on every PR
eval-framework-benchmark	Reproducible head-to-head benchmark vs DeepEval + RAGAS
multivon-guard (early access)	Local proxy that catches LLM coding agents leaking secrets / PII

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},
}