QA Copilot AI

classify_failure

Analyze automation error messages to classify Playwright and test failures using AI, identifying the failure type for faster debugging.

Instructions

Classify Playwright and automation failures using AI.

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": {
      "errorMessage": "<errorMessage>"
    },
    "name": "classify_failure"
  }
}

Input Schema

Name	Required	Description	Default
`errorMessage`	Yes	Playwright or Automation error message

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 31, 2026

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. It only states 'using AI', which hints at a model-based analysis, but does not disclose whether the operation is read-only, what output format to expect, or any error behavior.

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?

The description is a single concise sentence with no verbose padding. However, it lacks structural elements like examples or alternative usage, making it efficient but slightly under-specified.

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?

For a tool with one parameter and no output schema, the description gives minimal context. It doesn't explain what classification output will look like, what categories exist, or how this differs from sibling 'analyze_failure', leaving gaps for effective tool selection.

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 schema fully describes the only parameter 'errorMessage' with a clear description. The tool description adds no further meaning beyond that, so 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?

The description uses a specific verb 'classify' and names the resource 'Playwright and automation failures', which clearly indicates the tool's scope. However, it does not distinguish this from sibling tool 'analyze_failure', which could be seen as overlapping.

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?

No guidance is given on when to use this tool versus alternatives like analyze_failure, analyze_report, or suggest_fix. The usage context is only implicit (when you have an error message to classify).

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

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