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score_traces

Write scores back to Langfuse traces to annotate them with analysis findings. For example, mark failing traces with 'needs-review' for quick review.

Instructions

Write scores back to Langfuse traces. trace_ids: comma-separated.

Use this after analysis to annotate traces with findings. Example: score failing traces with 'needs-review'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commentNo
projectNo
trace_idsYes
score_nameYes
score_valueYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral details. It only says 'Write scores back', indicating mutation but not whether scores are overwritten, if any permissions are needed, or how errors are handled. The example is usage-related, not behavioral, leaving significant gaps.

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 concise and front-loaded with the core action. It includes the essential trace_ids format and a useful example, with no wasted sentences.

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?

For a 5-parameter write tool with no annotations and 0% schema coverage, the description is too sparse. It fails to cover key parameters like project and comment, and does not clarify side effects or the scoring semantics beyond the example. The presence of an output schema doesn't mitigate the missing parameter guidance.

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 description coverage is 0%, so the description must compensate. It only clarifies 'trace_ids: comma-separated', while the other parameters (score_name, score_value, comment, project) are not explained. The example hints at score_name ('needs-review') but does not systematically define each parameter.

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 states 'Write scores back to Langfuse traces' with a specific verb ('write') and resource ('Langfuse traces'). The example 'score failing traces with needs-review' clarifies the use case and differentiates it from read-only sibling tools like fetch_scores or fetch_traces.

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?

It explicitly says 'Use this after analysis' and provides a concrete example, giving clear temporal context. However, it does not name alternatives or state when not to use, slightly lowering the score.

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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