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mlflow-mcp-server

by us-all

summarize-run

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

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?

No annotations are provided, so the description carries full burden. It discloses that metric history and artifacts are optional, but lacks details on potential side effects, authentication needs, or behavior when includeMetricHistory is false (e.g., does it return only run info?). It is adequate but not exhaustive.

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?

The description is extremely concise—two sentences with zero wasted words. It front-loads the core purpose and then provides context on the efficiency benefit. Every sentence earns its place.

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?

Given the moderate complexity (6 parameters, no output schema, no annotations), the description adequately covers the tool's purpose and usage context. It omits details about output format but the schema covers all parameters. For a read-only aggregation tool, it is reasonably complete.

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 description coverage is 100%, so baseline is 3. The description does not add significant meaning beyond what is in the schema; it reiterates the optional nature of metric history and artifacts. The schema already contains detailed descriptions for each parameter, so the description adds minimal extra value.

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?

The description clearly states it provides an aggregated run view combining run info, optional metric history, and artifacts list in a single call. It explicitly distinguishes from siblings like get-run, get-metric-history, and list-artifacts by highlighting the reduction in round-trips.

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

Usage Guidelines4/5

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

The description provides strong usage guidance by explaining that it replaces 3-4 separate calls, implying efficiency when accessing combined data. However, it does not explicitly state when not to use it or mention alternative tools for specific use cases.

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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