Skip to main content
Glama

Executar testes

run_tests

Executes test suites for Cypress, Playwright, Jest, pytest, and more. Auto-detects the framework, optionally auto-fixes selector failures, and explains failure causes.

Instructions

Roda testes do projeto. Suporta: Cypress, Playwright, WebdriverIO, Jest, Vitest, Mocha, Appium, Detox, Robot Framework, pytest, e mais. Detecta automaticamente.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
specNoCaminho do spec (ex: cypress/e2e/test.cy.js).
suiteNoSuite ou pattern (ex: e2e, api).
deviceNoDevice/configuration para mobile. Se vazio, detecta de qa-lab-agent.config.json, wdio.conf ou .detoxrc.
frameworkNoFramework específico ou 'npm' para npm test.
autoFixSelectorNoSe true e falhar por seletor, aplica correção automaticamente e tenta novamente. Requer spec e API key. Default: true para mobile.
explainOnFailureNoSe true, quando falhar gera automaticamente: O que aconteceu, Por que falhou, O que fazer, Sugestão de correção. Requer API key.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
messageYes
exitCodeYes
runOutputNo
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the tool runs tests and auto-detects frameworks, which is useful, but it omits behavioral traits such as requiring an API key for auto-fix/explain features, potential side effects of test execution, or behavior on detection failure. Not misleading, but incomplete.

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 concise and front-loaded: it states the core purpose in the first sentence, lists supported frameworks in a compact manner, and adds the key auto-detection detail. Every word earns its place, with no fluff.

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?

Given the tool's complexity (many frameworks, six parameters, advanced features like autoFixSelector and explainOnFailure), the description is adequate but thin. It covers the main purpose and auto-detection but does not highlight the advanced capabilities or API key requirements, though an output schema exists to handle return values.

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%, so the baseline is 3. The description adds no parameter-specific information beyond the schema, but the schema itself already provides detailed descriptions for all six parameters, including enums and defaults.

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 runs project tests with a specific verb and resource, and lists supported frameworks plus auto-detection. This distinguishes it from sibling tools like watch_tests and run_linter by focusing on execution with broad framework support.

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 running tests with automatic framework detection, but it does not explicitly state when to use this tool versus alternatives like watch_tests or run_linter. No exclusions or when-not-to-use guidance is provided.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Wesley-Gomes93/qa-lab-agent-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server