MCP server exposing multivon-eval + pdfhell as agent-callable tools. Drop into Claude Desktop, Cursor, Cline, or any MCP-compatible AI coding agent.
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"multivon-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# multivon-mcp [](https://pypi.org/project/multivon-mcp) [](https://pypi.org/project/multivon-mcp) [](LICENSE) [](https://pepy.tech/project/multivon-mcp) **[Docs](https://docs.multivon.ai/mcp)** · [Website](https://multivon.ai/agents) · [PyPI](https://pypi.org/project/multivon-mcp) · [multivon-eval (engine)](https://github.com/multivon-ai/multivon-eval) · [Changelog](CHANGELOG.md) These 23 tools cover what an autonomous eval agent needs to do its job: discover its own capabilities (`eval_discover`), normalize traces from supported sources (`eval_ingest_trace`), and run evaluators against them, with calibration evidence specific to the tested task. We put the framework behind an MCP boundary because eval belongs in the agent's working loop, not behind a separate dashboard. An 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](https://modelcontextprotocol.io/)–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 self-verifying audit pack with a SHA-256 file manifest - Discover the full evaluation capability catalog as JSON No copy-paste, and no asking the agent to figure out the SDK calls from `python -c "..."` one-liners. > **Current release: 0.4.0.** Tested with MCP Python SDK 1.29.x, multivon-eval 0.18.0, and pdfhell 0.6.2. See the [changelog](CHANGELOG.md). ## Install ```bash pip install "multivon-mcp==0.4.0" ``` The next release carries this compatibilit…
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