mcp-lab-agent

by Wesley-Gomes93

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JavaScript

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Sugerir próximo teste a criar

qa_suggest_next_test

Analyzes your project's test coverage, detects gaps, and suggests the next test to write, focusing on critical flows.

Instructions

[IA PROATIVA] Analisa o projeto e sugere qual teste criar a seguir (baseado em cobertura, fluxos críticos, gaps detectados).

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_suggest_next_test"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`suggestions`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.3.1Aug 2, 2026

TDQS

A3.8/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'analisa' (analyzes) the project, which implies a read-only operation, but it does not explicitly confirm that it has no side effects, nor does it disclose what the suggestion output looks like or whether any project state is required. The description adds some context about criteria (coverage, critical flows, gaps) but not enough for full transparency.

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 sentence that includes a helpful tag ('IA PROATIVA') and a clear explanation of the tool's purpose and reasoning basis. There is no wasted language, and the structure is efficient and front-loaded.

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 that the tool has no parameters and an output schema exists, the description is adequately complete. It explains the tool's core behavior and the criteria used for the suggestion. It could mention a prerequisite (e.g., project detection), but the lack of such is not a significant gap given the simplicity of the tool and the presence of sibling tools that handle project setup.

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 tool has zero parameters, and the schema coverage is 100% (trivially), so the baseline is 4. The description adds context about the analysis criteria, which is useful for understanding what the tool considers, but there are no parameter-specific semantics to clarify. The score reflects the baseline for no-parameter tools.

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 function: it analyzes the project and suggests which test to create next, based on coverage, critical flows, and gaps. This specific verb+resource ('sugere qual teste criar a seguir') distinguishes it from sibling tools like generate_tests (which generates tests) or write_test (which writes a specific test).

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 when to use the tool (when needing a suggestion for the next test) and mentions the basis for the suggestion, but it does not explicitly state when not to use it or provide alternatives. The '[IA PROATIVA]' tag hints at proactive use but lacks elaboration, leaving the guidance implicit rather than explicit.

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