mcp-lab-agent

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Escrever arquivo de teste

write_test

Save test specs to disk with no manual setup: auto-detects framework, folder, and extension for Cypress, Playwright, WDIO, Appium, Robot, pytest, and more.

Instructions

Grava spec no disco. Suporta QUALQUER framework (Cypress, Playwright, WDIO, Appium, Robot, pytest, etc.). Detecta automaticamente pasta e extensão.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "content": "<content>"
    },
    "name": "write_test"
  }
}

Input Schema

Name	Required	Description
`name`	Yes	Nome do arquivo (ex: login-test, logout_spec).
`subdir`	No	Subpasta (ex: e2e, api). Default: raiz da pasta de testes.
`content`	Yes	Conteúdo do spec.
`framework`	No	Framework (detectado automaticamente se omitido).

Output Schema

Name	Required	Description	Default
`ok`	Yes
`path`	No
`error`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.3.1Aug 2, 2026

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are provided, so the description carries full responsibility. It discloses that it writes to disk and auto-detects path, but omits critical side effects such as whether it overwrites existing files, creates missing directories, or what happens on error. The return value is not described, though an output schema may cover that.

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 three short sentences, each adding valuable information: the core action, framework flexibility, and automatic path detection. There is no redundancy or unnecessary detail.

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

Completeness2/5

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

While the schema covers parameter documentation, the description lacks essential behavioral context for a write operation, specifically overwrite semantics and directory creation. Since an output schema exists, return values are handled, but the side effects remain undocumented, leaving the tool only minimally complete.

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?

The input schema provides full descriptions for all four parameters (100% coverage), so the baseline is 3. The description does not add any extra meaning or usage details beyond what the schema already includes.

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 uses the specific verb 'Grava' (writes) with the resource 'spec' (test file), and adds that it supports any framework and auto-detects folder and extension. This clearly distinguishes it from sibling tools like read_file or run_tests.

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?

Usage is implied: use this when you need to write a test spec file. However, it does not explicitly state when not to use it, nor does it mention alternative tools like generate_tests or create_test_template. It also lacks context on prerequisites like needing an existing project.

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