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mlflow-mcp-server

log-inputs

Log dataset inputs to an MLflow run to track which datasets were used for model training or evaluation.

Instructions

Log dataset inputs to a run

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
runIdYes
datasetsYesDataset input records
Behavior2/5

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

Beyond annotations indicating a write operation (readOnlyHint=false) and open schema (openWorldHint=true), the description does not disclose behavior like whether data is appended or overwritten, or effects of invalid runId.

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

Conciseness3/5

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

Single sentence is concise, but it is too brief to provide substantial value. Could be expanded without becoming verbose.

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

Completeness2/5

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

No explanation of what 'logging' entails (e.g., persistence, association with run), and no output schema to set expectations. Incomplete for an agent to use correctly, especially with many sibling log tools.

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

Parameters2/5

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

Schema coverage is 50% with runId undocumented. Description omits any details about the parameters (e.g., format of runId, structure of dataset objects), failing to compensate for the schema gap.

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

Purpose4/5

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

Description clearly states the action (log), resource (dataset inputs), and target (a run), distinguishing from sibling tools like log-metric and log-param. However, 'dataset inputs' is somewhat vague.

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

Usage Guidelines2/5

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

No guidance on when to use this tool vs. alternatives (e.g., log-batch) or when not to use it. No exclusions or prerequisites mentioned.

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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