nvidia/accessing-mlflow
Query and browse evaluation results stored in MLflow. Use when the user wants to look up runs by invocation ID, compare metrics across models, fetch artifacts (configs, logs, results), or set up the MLflow MCP server. ALWAYS triggers on mentions of MLflow, experiment results, run comparison, invocation IDs in the context of results, or MLflow MCP setup.
npx skills add https://github.com/NVIDIA/Model-Optimizer --skill accessing-mlflowmlflow-mcp gives agents direct access to MLflow — query runs, compare metrics, browse artifacts, all through natural language.
When the user provides a hex ID (e.g. 71f3f3199ea5e1f0) without specifying what it is, assume it is an invocation_id (not an MLflow run_id). An invocation_id identifies a launcher invocation and is stored as both a tag and a param on MLflow runs. One invocation can produce multiple MLflow runs (one per task). You may need to search across multiple experiments if you don't know which experiment the run belongs to.
# Find runs by invocation_id
MLflow:search_runs_by_tags(experiment_id, {"invocation_id": "<invocation_id>"})
# Query for example model/task runs
MLflow:query_runs(experiment_id, "tags.model LIKE '%<model>%'")
MLflow:query_runs(experiment_id, "tags.task_name LIKE '%<task_name>%'")
# Get a config from run's artifacts
MLflow:get_artifact_content(run_id, "config.yml")
# Get nested stats from run's artifacts
MLflow:get_artifact_content(run_id, "artifacts/eval_factory_metrics.json")
NOTE: You WILL NOT find PENDING, RUNNING, KILLED, or FAILED runs in MLflow! Only SUCCESSFUL runs are exported to MLflow.
When comparing metrics across runs, fetch the data via MCP, then run the computation in Python for exact results rather than doing math in-context:
uv run --with pandas python3 << 'EOF'
import pandas as pd
# ... compute deltas, averages, etc.
EOF
<harness>.<task>/
├── artifacts/
│ ├── config.yml # Fully resolved config used during the evaluation
│ ├── launcher_unresolved_config.yaml # Unresolved config passed to the launcher
│ ├── results.yml # All results in YAML format
│ ├── eval_factory_metrics.json # Runtime stats (latency, tokens count, memory)
│ ├── report.html # Request-Response Pairs samples in HTML format (if enabled)
│ └── report.json # Request-Response Pairs samples in JSON format (if enabled)
└── logs/
├── client-*.log # Evaluation client
├── server-*-N.log # Deployment per node
├── slurm-*.log # Slurm job
└── proxy-*.log # Request proxy
If the MLflow MCP server fails to load or its tools are unavailable:
uvx not found — install uv: curl -LsSf https://astral.sh/uv/install.sh | sh
For Claude Code — add to .claude/settings.json (project or user level), under "mcpServers":
"MLflow": {
"command": "uvx",
"args": ["mlflow-mcp"],
"env": {
"MLFLOW_TRACKING_URI": "https://<your-mlflow-server>/"
}
}
For Cursor — edit ~/.cursor/mcp.json (Settings > Tools & MCP > New MCP Server):
{
"mcpServers": {
"MLflow": {
"command": "uvx",
"args": ["mlflow-mcp"],
"env": {
"MLFLOW_TRACKING_URI": "https://<your-mlflow-server>/"
}
}
}
}
Take nvidia/accessing-mlflow from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.