mcp-lab-agent

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Prever quais testes vão ficar flaky

qa_predict_flaky

Analyze existing test code to predict which tests are likely to become flaky by detecting fragile selectors, inadequate waits, and external dependencies.

Instructions

[PREDIÇÃO] Analisa testes existentes e prevê quais têm maior chance de se tornarem flaky (baseado em padrões: seletores frágeis, waits inadequados, dependências externas).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "qa_predict_flaky"
  }
}

Input Schema

Name	Required	Description	Default
`testFile`	No	Arquivo específico (opcional). Se omitido, analisa todos.

Output Schema

Name	Required	Description	Default
`predictions`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.3.1Aug 2, 2026

TDQS

A3.6/5.0

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 traits. It states the analytical nature (predicts flakiness) but does not mention whether the tool is read-only, has side effects, requires specific permissions, or how it handles missing files. For a tool that likely inspects tests, this is a significant gap.

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 a single, focused sentence, front-loaded with the '[PREDIÇÃO]' tag. Every part adds value: purpose, basis, and patterns. No wasted words.

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 tool's simplicity (one optional parameter) and that an output schema exists (though not shown), the description need not explain return values. The purpose and analysis basis are clear. It could be more complete by mentioning default behavior (analyzes all when testFile omitted), but the schema covers that.

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%, with the only parameter testFile clearly described as optional and specifying behavior when omitted. The description itself adds no extra parameter semantics, but the schema already provides sufficient meaning, meeting the baseline.

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 tool's purpose: analyzing existing tests to predict flakiness based on specific patterns (fragile selectors, inadequate waits, external dependencies). It uses a specific verb ('analisa', 'prevê') and resource ('testes existentes'), distinguishing it from other QA tools like run_tests or analyze_failures.

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 description implies usage for analysis and prediction of flaky tests, but it does not explicitly state when to use this tool versus alternatives like analyze_failures or qa_health_check. It gives context (existing tests, prediction) but no exclusions or explicit alternative references.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.