mlops-mcp-server 0.1.0

MCP server for common MLOps workflows

MLOps MCP Server

MCP server for common MLOps workflows

An MCP (Model Context Protocol) 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.

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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 discovery, and 15 domain modules you activate on demand to avoid flooding the agent context window.

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Installation

Install directly from GitHub (available now)

pip install git+https://github.com/anant-patankar/mlops-mcp-server.git

Using uv:

uv add git+https://github.com/anant-patankar/mlops-mcp-server.git

Install from PyPI (coming soon)

# Core install
pip install mlops-mcp-server

# With optional extras
pip install mlops-mcp-server[mlflow]
pip install mlops-mcp-server[mlflow,compare,notebooks,parquet]

Using uv:

uv add mlops-mcp-server
uv add "mlops-mcp-server[mlflow,compare,notebooks,parquet]"

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Usage

Claude Desktop

Add to your Claude Desktop MCP config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

First install the package, then add to your Claude Desktop config:

{
  "mcpServers": {
    "mlops": {
      "command": "mlops-mcp-server"
    }
  }
}

Restart Claude Desktop. The server starts automatically when Claude connects.

Example prompts

Once connected, you can ask Claude things like:

"Show me all experiment runs, find the one with the best validation accuracy, and register that model in the registry."

"Profile data/train.csv, check it against schema.yaml, then split it 70/15/15 and save to data/splits/."

"Create a pipeline with three stages: preprocess → train → evaluate, validate it for cycles, and show me the Mermaid diagram."

Running directly

mlops-mcp-server

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Optional Dependencies

Extra	Installs	Enables
mlflow	mlflow	MLflow tracking and registry tools
compare	deepdiff	Structured diff in compare_runs
notebooks	nbformat	Notebook summary in get_notebook_summary
parquet	pyarrow	Parquet read/write in dataset tools
models	joblib, onnx, safetensors	Model file format support
validation	pandera, scipy	Schema validation and drift statistics
templates	jinja2	Jinja2 templates in create_model_card

All extras are optional — the server runs without them and returns clear errors if a missing dependency is needed.

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License

MIT — see LICENSE.