mcp-lab-agent

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Estatísticas de aprendizado

qa_learning_stats

Retrieves QA agent learning metrics: tests generated, first-attempt success rate, and corrections applied. Tracks testing performance to identify areas for improvement.

Instructions

[MÉTRICAS] Retorna métricas de aprendizado do agente: quantos testes gerados, taxa de sucesso na primeira tentativa, correções aplicadas, etc.

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_learning_stats"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`timingFixes`	Yes
`selectorFixes`	Yes
`testsGenerated`	Yes
`totalLearnings`	Yes
`successfulFixes`	Yes
`firstAttemptSuccessRate`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.3.1Aug 2, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are present, so the description carries the full burden. It implies a read-only operation ('Retorna') and lists specific metrics, but it does not disclose potential side effects, dependencies, or limitations. Basic transparency is provided, but not deeply.

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, front-loaded sentence with a [MÉTRICAS] tag and concrete examples. It is concise and every element contributes meaning, with no filler.

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?

For a zero-parameter metrics tool with an output schema, the description adequately conveys what is returned. However, it omits usage context and relationships to sibling tools, leaving some gaps in completeness.

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

Parameters4/5

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

The input schema has zero parameters, so the baseline is 4. The description does not need to explain parameter details, and it doesn't introduce ambiguity. The examples of metrics add context but are not parameter-related.

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?

The description clearly states the tool returns agent learning metrics and provides concrete examples (tests generated, first-attempt success rate, corrections). However, it does not explicitly distinguish itself from the sibling tool get_learning_report, which might overlap in purpose.

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 is given about when to use this tool versus alternatives like get_learning_report or qa_health_check. The description only states what it does, leaving the agent to infer usage context.

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