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agy_validate_tests

Analyze test and production code to detect fragile assertions, uncovered edge cases, and state leakage, ensuring test suite robustness.

Instructions

Analisa código de teste e de produção via Antigravity (agy CLI) para identificar asserções frágeis, casos de borda não cobertos, vazamento de estado e robustez da suíte.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModelo Google Gemini a utilizar. Padrão: gemini-3.7-flash-medium.gemini-3.7-flash-medium
promptYesCódigo dos testes, especificação de requisitos e detalhes dos componentes a serem validados.
directoryNoDiretório base do projeto para contexto.

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 burden and falls short. It discloses the execution vehicle (agy CLI / Antigravity), which is useful context, but says nothing about whether files are modified, whether a report is returned, auth/API-key needs, or cost/latency of an LLM call.

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 the verb and resource first, followed by the concrete findings it produces. No filler or restatement of the name, though the field list makes it dense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description should hint at the return shape and behavioral envelope, and it does neither. It adequately covers the analysis scope for a 3-parameter tool, but an agent cannot tell whether this just returns text, writes artifacts, or alters the working directory.

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% and all three parameters (model, prompt, directory) are documented in the schema itself, including the enum and default. The description adds no extra meaning about how prompt or directory should be formatted, so the baseline 3 applies.

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 (analisa) and resource (código de teste e de produção) and names the exact analysis dimensions it will report on: asserções frágeis, casos de borda não cobertos, vazamento de estado, robustez da suíte. It is clear and concrete, but it never distinguishes itself from the sibling agy_review, which an agent could easily mistake for this task.

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?

There is no explicit when-to-use, when-not-to-use, or alternative guidance. With siblings like agy_review and agy_plan, the agent must infer from the name alone that test/quality audit is this tool's lane, and nothing excludes using agy_review for the same input.

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