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":"mlops-mcp-server","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.
## MLOps MCP Server [](https://pypi.org/project/mlops-mcp-server/)   **MCP server for common MLOps workflows** An [MCP (Model Context Protocol)](https://modelcontextprotocol.io) server that gives AI assistants like Claude direct access to MLOps workflows — experiment tracking, model registry, dataset management, pipeline orchestration, lineage tracing, and more. Wraps DVC, MLflow, and Git rather than replacing them. --- ## Features - **Experiment tracking** — create runs, log params/metrics/artifacts, compare runs, find best run, export to CSV - **Model registry** — register, promote, tag, deprecate, and compare model versions; generate model cards - **Dataset management** — profile datasets, validate schemas, detect statistical drift (KS-test), split, merge, and generate dataset cards - **Pipeline management** — create, validate, and visualize DAG pipelines as Mermaid diagrams; cycle detection via Kahn's algorithm - **Data lineage** — record artifact lineage, trace provenance with BFS, visualize as Mermaid graphs, check integrity - **File operations** — full file/directory CRUD, content search, disk usage, batch operations - **Project scaffolding** — 7 ML project templates with component injection and structure validation - **Documentation generation** — model cards, dataset cards, experiment reports, pipeline docs, project READMEs, API docs - **MLflow integration** — optional; wraps tracking, registry, and artifact operations - **DVC integration** — optional; wraps data versioning and pipeline reproduction - **Git operations** — optional; status, add, commit, log, .gitignore generation - **Environment tools** — scan imports, generate requirements, check conflicts, create conda env files Tools are registered in two tiers: a small set of always-on tools for quick file ops and di…
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