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mlflow-mcp-server

summarize-run

Read-only

Aggregate run details, metric history, and artifacts in one API call, reducing multiple round-trips for faster insights.

Instructions

Aggregated run view: run info + (optional) metric history + (optional) artifacts list in a single call. Replaces 3-4 round-trips of get-run + get-metric-history (per metric) + list-artifacts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
runIdYesRun ID
includeMetricHistoryNoInclude time-series metric history (default false — set true for plot-ready data)
metricKeysNoSpecific metric keys to fetch history for (default: all metrics on the run)
includeArtifactsNoInclude artifacts file list (default true)
artifactPathNoOptional sub-path within artifacts to list
extractFieldsNoComma-separated dotted paths to project from response. Use `*` as wildcard.
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety and variability. The description adds context about aggregation but no new behavioral traits beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first defines purpose, second explains benefit. Extremely concise and front-loaded with no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description hints at return structure ('run info + metric history + artifacts list') which is adequate given the open-world hint and the fact that components are well-known endpoints. Lacks explicit mention of extraction fields but parameters cover that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear parameter descriptions. The tool description does not add extra semantic detail beyond the schema; baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states verb (summarize), resource (run), and scope: 'run info + (optional) metric history + (optional) artifacts list'. It distinguishes from siblings like get-run, get-metric-history, list-artifacts by highlighting the aggregation benefit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Replaces 3-4 round-trips', telling when to use (to avoid multiple calls) and implying when not to (if only one component needed). Also describes optional parameters.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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