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mlflow-mcp-server

log-feedback

Record evaluation feedback (score, label, or judgment) on a trace to capture human or AI assessments for analysis.

Instructions

Log evaluation feedback (score or judgment) on a trace

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
traceIdYesTrace ID this feedback is for
nameYesFeedback name (e.g. 'helpfulness')
valueYesFeedback value (score, label, etc.)
rationaleNoFree-form explanation
sourceNo
metadataNo
Behavior3/5

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

Annotations indicate readOnlyHint=false (write operation) and openWorldHint=true (accepts unknown properties). The description confirms it logs feedback but adds no additional behavioral details such as whether feedback is appended, overwritten, or has any side effects. With annotations present, the bar is lower, but context about potential overwriting or append behavior would improve transparency.

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

Conciseness4/5

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

The description is a single, front-loaded sentence with no wasted words. It is appropriately sized for the tool's simplicity, though it could be slightly more informative about usage context.

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?

Given the tool has 6 parameters (3 required), nested objects, and no output schema, the description omits important context like how feedback is associated with traces, what the return value is, or prerequisites (e.g., trace must exist). It does not fully address the complexity.

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?

The description adds minimal meaning beyond the input schema. The phrase 'score or judgment' hints at the value parameter's semantics, but the schema already describes it as 'score, label, etc.'. Schema description coverage is 67%, so baseline is 3; the description does not compensate for the undocumented parameters like rationale or source format.

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 the action 'Log' and the resource 'evaluation feedback (score or judgment)' and the target 'on a trace'. It uses a specific verb and resource, and distinguishes this tool from sibling logging tools like log-metric or log-param.

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?

The description provides no guidance on when to use this tool versus alternatives. Siblings include log-batch, log-expectation, log-inputs, log-metric, log-param, but no differentiation is 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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