mcp-lab-agent

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Relatório de evolução e aprendizado

get_learning_report

Generate a learning progress report that summarizes types of learnings, tracks evolution over time, and provides recommendations for code improvement.

Instructions

Gera relatório de evolução dos aprendizados: resumo por tipo, evolução no tempo e recomendações para aprimorar o código.

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": "get_learning_report"
  }
}

Input Schema

Name	Required	Description	Default
`format`	No	summary = resumo executivo, full = relatório completo com recomendações. Default: summary

Output Schema

Name	Required	Description	Default
`byType`	Yes
`summary`	Yes
`evolution`	No
`recommendations`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.3.1Aug 2, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the report's content (summary by type, evolution, recommendations) and implies a read-only generation, but it does not explain whether it requires prior data, whether it performs expensive analysis, or how it handles empty states. This is adequate for a simple report tool but lacks depth.

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, information-dense sentence that covers the tool's purpose and key output aspects without redundancy or 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?

Given the tool's simplicity (one optional parameter, output schema provided), the description is mostly complete. It could be enhanced by mentioning when to use this over similar learning-related tools, but the existing text sufficiently explains what the report contains.

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 coverage is 100% for the single 'format' parameter, including its enum values and default. The description does not add any additional meaning beyond the schema, so the baseline score of 3 is appropriate.

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 generates a learning evolution report, specifying its contents: summary by type, evolution over time, and recommendations. This use of a specific verb 'Gera' and resource 'relatório de evolução dos aprendizados' makes the purpose unambiguous, even among siblings like qa_learning_stats.

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 such as qa_learning_stats or qa_health_check. It does not mention any prerequisites, context, or scenarios where this report is preferred.

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