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":"mcp-auditor","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.
# mcp-auditor Agentic security testing for MCP servers. [](https://pypi.org/project/mcp-auditor/) [](https://github.com/mkrtchian/mcp-auditor/actions/workflows/ci.yml) [](https://github.com/mkrtchian/mcp-auditor/actions/workflows/evals.yml) [](https://langchain-ai.github.io/langgraph/)  ## The problem MCP servers expose tools that LLM agents call with untrusted input. Testing that surface by hand does not scale, and generic fuzzers know nothing about the MCP protocol or its threat model. Most MCP security tooling today works from one of two angles. **Static analysis** reads the declared surface (tool descriptions and JSON schemas) and flags poisoning before any call is made. **Runtime observation** watches a server behave, either as a proxy on live traffic or in a sandbox that builds and runs it, and reports what the process does at the syscall and network level. Both watch a server doing its normal work. `mcp-auditor` sends the server hostile input instead. It connects to a live server, generates adversarial payloads, executes them, and judges whether the responses reveal vulnerabilities. It probes for input validation failures, injection, information leakage, error handling gaps, and resource abuse, and produces structured verdicts with justifications and severity ratings. ## Quick start ```bash # Set your API key (create one at platform.openai.com/api-keys) export OPENAI_API_KEY=your-key-here # Audit an MCP server (the server runs in a Docker container, so Docker must be running) mkdir -p /tmp/sandbox uvx mcp-auditor run -- npx @modelcontextprotocol/server-filesystem /tmp/sandbox # Or launch it on this host instead…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.