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nks1097
by nks1097

grafana_comparar_traces

Compare two distributed traces to pinpoint differences between fast and slow or failing executions, helping diagnose latency and error causes.

Instructions

  1. Comparar dois traces distribuídos (ex: execução rápida vs execução lenta com erro).

Input Schema

NameRequiredDescriptionDefault
trace_id_1Yes
trace_id_2Yes
datasource_uidNo

Output Schema

NameRequiredDescriptionDefault
erroNoMensagem de erro amigável caso haja falha
dadosNoCarga útil da resposta estruturada
avisosNoLista de alertas e avisos operacionais
sucessoNoIndica se a operação foi bem-sucedida
metadadosNoMetadados da execução
id_requisicaoNoIdentificador único da operação

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full disclosure burden, and it does not say whether this is a read-only operation, what form the comparison takes, or whether both traces must belong to the same service/datasource. The presence of an output schema offsets some of this, but the behavioral profile of a comparison operation is left essentially unexplained.

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?

A single front-loaded sentence with no padding in the body. The leading '45.' is an artifact from a list and is pure noise, but otherwise the sentence is tight.

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?

Return values are covered by the output schema, so that is not a gap. But for a 3-parameter comparison tool with no annotations and 0% schema coverage, the description provides almost none of the context needed to call it correctly — notably what datasource_uid does and what the two traces must have in common.

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% and the description only implies that two trace identifiers are needed ('dois traces'), adding no format, source, or constraint details. The third parameter, datasource_uid, is completely undocumented in both the schema and the description.

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?

States a specific verb (comparar) and specific resource (dois traces distribuídos), plus a concrete example use case (fast execution vs slow erroring execution). It does not, however, distinguish itself from nearby siblings like grafana_obter_detalhes_trace or grafana_correlacionar_logs_metricas_traces, so the agent must infer the boundary.

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

Usage Guidelines3/5

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

The parenthetical example ('execução rápida vs execução lenta com erro') implies the scenario where this tool is useful, which is a genuine usage hint. There is no explicit when-not-to-use guidance and no named alternative for single-trace inspection.

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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