Is io.github.us-all/mlflow safe to use?

io.github.us-all/mlflow has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.

How do I install io.github.us-all/mlflow?

io.github.us-all/mlflow supports copy-paste install configs on its MCPpedia page for Claude Desktop, Cursor, and Claude Code. Scroll to the Quick Install section and select your client.

What can io.github.us-all/mlflow do?

io.github.us-all/mlflow provides 10 tools: summarize-experiment, summarize-run, compare-runs, search-tools, get-run and 5 more. See the full tools list on the server page for descriptions and parameters.

What AI clients work with io.github.us-all/mlflow?

io.github.us-all/mlflow is compatible with claude-desktop, cursor, claude-code. It uses stdio and sse and http transport.

Is io.github.us-all/mlflow actively maintained?

io.github.us-all/mlflow is actively maintained — last commit was 10 days ago. It has 1 GitHub stars.

io.github.us-all/mlflow

@us-all/mlflow-mcp

MLflow MCP — experiments, runs, registered models, versions, traces, assessments (MLflow 3)

1 76/wk 10 tools GitHub npm

No known CVEs

No license

Actively maintained

Last commit 10d ago

Works with most clients

Transport: stdio, sse, http

10 tools · ~455 tok

Grade A · 0.2% of 200K ctx

MCPpedia last refreshed this data 21h ago

Edit this pageView history

Other

Step 1

Install in your client

Config is the same across clients — only the file and path differ.

Supported in Claude Desktopstdio, sse, http · Node 18+

Paste into ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "mlflow": {
      "env": {
        "MLFLOW_TRACKING_URI": "http://localhost:5000"
      },
      "args": [
        "-y",
        "@us-all/mlflow-mcp"
      ],
      "command": "npx"
    }
  }
}

Are you the author?

Add this badge to your README to show your security score and help users find safe servers.

Embed in your READMEAbout badges →

[![MCPpedia Score](https://mcppedia.org/api/badge/io-github-us-all-mlflow)](https://mcppedia.org/s/io-github-us-all-mlflow)

Read me

What io.github.us-all/mlflow does

The widest-coverage MLflow MCP — including MLflow 3 traces, prompt-optimization, webhooks, and Databricks trace attachments that no other MCP exposes. > 82 tools across experiments, runs, registry, logged models, traces, assessments, webhooks, prompt-optimization. Aggregation tools (summarize-experiment, summarize-run) fold 3–5 round-trips into one structured response with already-fetched metric stats.

Test This Server

Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.

npx -y '@us-all/mlflow-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"

After testing, let us know if it worked:

Loading README…

Scored, not listed

Why this score

Five weighted categories — click any category to see the underlying evidence.

Score breakdown

87/100across 5 weighted dimensions

How we score →

0255075100

−13

Security

Maintenance

Efficiency

Documentation

Compatibility

Categoriesclick a row to see evidence

Security

OSV.dev

No known CVEs.

Checked @us-all/mlflow-mcp against OSV.dev.

Inventory

Tools (10)

Click any tool to inspect its schema.

~455 tokens total

Inventory

Resources (6)

mlflow run

MLflow run resource

mlflow://run/{runId}

mlflow experiment

MLflow experiment resource

mlflow://experiment/{expId}

mlflow run artifacts

MLflow run artifacts resource

mlflow://run/{runId}/artifacts

mlflow experiment runs

MLflow experiment runs resource

mlflow://experiment/{expId}/runs

mlflow registered model versions

MLflow registered model versions resource

mlflow://registered-model/{name}/versions

mlflow trace

MLflow trace resource

mlflow://trace/{traceId}

Inventory

Prompts (4)

debug-failed-traces

Find failed traces, group failure modes

promote-best-run

Find best run, register model, and set champion alias

compare-top-runs

Compare top runs by metric

annotate-trace-quality

Highlight slow spans and failed feedback annotations

Community

Reviews

Be the first to review

Have you used this server?

Share your experience — it helps other developers decide.

How easy was setup?Did it work reliably?How was the documentation?

Frequently Asked Questions

Is io.github.us-all/mlflow safe to use?: io.github.us-all/mlflow has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.
How do I install io.github.us-all/mlflow?: io.github.us-all/mlflow supports copy-paste install configs on its MCPpedia page for Claude Desktop, Cursor, and Claude Code. Scroll to the Quick Install section and select your client.
What can io.github.us-all/mlflow do?: io.github.us-all/mlflow provides 10 tools: summarize-experiment, summarize-run, compare-runs, search-tools, get-run and 5 more. See the full tools list on the server page for descriptions and parameters.
What AI clients work with io.github.us-all/mlflow?: io.github.us-all/mlflow is compatible with claude-desktop, cursor, claude-code. It uses stdio and sse and http transport.
Is io.github.us-all/mlflow actively maintained?: io.github.us-all/mlflow is actively maintained — last commit was 10 days ago. It has 1 GitHub stars.

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Community

Discussion

Start a conversation

Ask a question, share a tip, or report an issue.

Has anyone used this with Cursor?How do you handle auth?Any alternatives?

Edit this pageView history

Other

Step 1

Install in your client

Config is the same across clients — only the file and path differ.

Supported in Claude Desktopstdio, sse, http · Node 18+

Paste into ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "mlflow": {
      "env": {
        "MLFLOW_TRACKING_URI": "http://localhost:5000"
      },
      "args": [
        "-y",
        "@us-all/mlflow-mcp"
      ],
      "command": "npx"
    }
  }
}

Are you the author?

Add this badge to your README to show your security score and help users find safe servers.

Embed in your READMEAbout badges →

[![MCPpedia Score](https://mcppedia.org/api/badge/io-github-us-all-mlflow)](https://mcppedia.org/s/io-github-us-all-mlflow)

Read me

What io.github.us-all/mlflow does

The widest-coverage MLflow MCP — including MLflow 3 traces, prompt-optimization, webhooks, and Databricks trace attachments that no other MCP exposes. > 82 tools across experiments, runs, registry, logged models, traces, assessments, webhooks, prompt-optimization. Aggregation tools (summarize-experiment, summarize-run) fold 3–5 round-trips into one structured response with already-fetched metric stats.

Test This Server

Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.

npx -y '@us-all/mlflow-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"

After testing, let us know if it worked:

README

MLflow MCP Server

The widest-coverage MLflow MCP — including MLflow 3 traces, prompt-optimization, webhooks, and Databricks trace attachments that no other MCP exposes.

82 tools across experiments, runs, registry, logged models, traces, assessments, webhooks, prompt-optimization. Aggregation tools (summarize-experiment, summarize-run) fold 3–5 round-trips into one structured response with already-fetched metric stats.

What it does that others don't

Full coverage — only third-party MLflow MCP shipping prompt-optimization-jobs (5 tools), webhooks (6), MLflow 3 LoggedModel (8), and Databricks trace attachments (list-trace-attachments, get-trace-attachment — Databricks MLflow only; OSS returns 404).
Aggregation tools — summarize-experiment returns experiment + topN runs + metric stats (min/max/mean) in one call from already-fetched data, zero extra round-trips. summarize-run dedups metricHistory.history.*.key (~100KB savings on 4k-point series).
MCP Prompts (4) — debug-failed-traces, promote-best-run, compare-top-runs, annotate-trace-quality. Workflow templates the model invokes directly.
MCP Resources (6) — mlflow://run/{runId}, mlflow://experiment/{expId}, mlflow://run/{runId}/artifacts, mlflow://experiment/{expId}/runs, mlflow://registered-model/{name}/versions, mlflow://trace/{traceId}.
Token-efficient by design — extractFields projection on get-run / search-runs / search-traces / get-trace / fat reads, MLFLOW_TOOLS / MLFLOW_DISABLE 8 categories, search-tools meta-tool.
Apps SDK card — compare-runs renders as a side-by-side card on ChatGPT clients (run summary + metric/param tables with diff highlight) via _meta["openai/outputTemplate"]. Claude clients receive the same JSON content.
stdio + Streamable HTTP — defaults to stdio. Set MCP_TRANSPORT=http for ChatGPT Apps SDK or remote clients (Bearer auth via MCP_HTTP_TOKEN).

Try this — 5 prompts

Connect the server to Claude Desktop or Claude Code, then paste any of these:

Best run — "In the customer-churn-v3 experiment, find the run with the highest val_accuracy. Show its hyperparameters and metric history."
Failure mode clustering — "Find traces with status=ERROR from the last 24h in experiment 12. Group the failures by exception type and surface the 3 most common."
Run comparison — "Compare the top 5 runs of experiment 12 by validation_loss. Show differing hyperparameters in a table."
Model promotion — "Get the latest version of recommendation_v2 registered model with the champion alias. Show its training metrics + lineage to the source run."
Trace deep-dive — "Pull trace tr-abc123. Highlight slow spans and any failed feedback annotations." (Add list-trace-attachments on Databricks workspaces.)

When to use this vs alternatives

	Official `mlflow[mcp]`	kkruglik/mlflow-mcp	`@us-all/mlflow-mcp` (this)
Tool count	~9 (trace-only)	~25	78
MLflow 3 LoggedModel	❌	✅	✅
Trace attachments	❌	❌	✅ Databricks only
Prompt-optimization-jobs	❌	❌	✅
Webhooks	❌	❌	✅
Aggregation tools	❌	❌	✅ `summarize-experiment`, `summarize-run`
MCP Prompts	❌	✅	✅
MCP Resources	❌	❌

... View full README on GitHub

Loading README…

Scored, not listed

Why this score

Five weighted categories — click any category to see the underlying evidence.

Score breakdown

87/100across 5 weighted dimensions

How we score →

0255075100

−13

Security

Maintenance

Efficiency

Documentation

Compatibility

Categoriesclick a row to see evidence

Security

OSV.dev

No known CVEs.

Checked @us-all/mlflow-mcp against OSV.dev.

Inventory

Tools (10)

Click any tool to inspect its schema.

~455 tokens total

Inventory

Resources (6)

mlflow run

MLflow run resource

mlflow://run/{runId}

mlflow experiment

MLflow experiment resource

mlflow://experiment/{expId}

mlflow run artifacts

MLflow run artifacts resource

mlflow://run/{runId}/artifacts

mlflow experiment runs

MLflow experiment runs resource

mlflow://experiment/{expId}/runs

mlflow registered model versions

MLflow registered model versions resource

mlflow://registered-model/{name}/versions

mlflow trace

MLflow trace resource

mlflow://trace/{traceId}

Inventory

Prompts (4)

debug-failed-traces

Find failed traces, group failure modes

promote-best-run

Find best run, register model, and set champion alias

compare-top-runs

Compare top runs by metric

annotate-trace-quality

Highlight slow spans and failed feedback annotations

Community

Reviews

Be the first to review

Have you used this server?

Share your experience — it helps other developers decide.

How easy was setup?Did it work reliably?How was the documentation?

Frequently Asked Questions

Is io.github.us-all/mlflow safe to use?: io.github.us-all/mlflow has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.
How do I install io.github.us-all/mlflow?: io.github.us-all/mlflow supports copy-paste install configs on its MCPpedia page for Claude Desktop, Cursor, and Claude Code. Scroll to the Quick Install section and select your client.
What can io.github.us-all/mlflow do?: io.github.us-all/mlflow provides 10 tools: summarize-experiment, summarize-run, compare-runs, search-tools, get-run and 5 more. See the full tools list on the server page for descriptions and parameters.
What AI clients work with io.github.us-all/mlflow?: io.github.us-all/mlflow is compatible with claude-desktop, cursor, claude-code. It uses stdio and sse and http transport.
Is io.github.us-all/mlflow actively maintained?: io.github.us-all/mlflow is actively maintained — last commit was 10 days ago. It has 1 GitHub stars.

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