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QA Copilot AI

QA Copilot AI

by Sham-coder16
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

analyze_failure

Pinpoint test failure root causes and get actionable fix suggestions using AI analysis.

Instructions

Analyze test failure 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": {
      "error": "<error>"
    },
    "name": "analyze_failure"
  }
}

Input Schema

NameRequiredDescriptionDefault
errorYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.1/5.0
Behavior1/5

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 only states 'Analyze test failure using AI' without mentioning any side effects, return behavior, external calls, or required permissions. Users are left completely in the dark about what the tool actually does at runtime.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence, which is concise, but it is under-specified rather than appropriately brief. It omits essential details about usage and behavior, making it more of a placeholder summary than a helpful tool description.

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

Completeness1/5

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

Given the tool has one parameter, no output schema, and no annotations, the description is severely incomplete. It does not explain what the analysis output looks like, how the error should be formatted, or what the AI does with it. With several sibling tools present, the lack of contextual detail makes it difficult to select and use this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one required parameter 'error' with zero description coverage. The tool description does not mention or explain this parameter, its format, or expected content. Since schema coverage is 0% and the description fails to compensate, parameter semantics are entirely undocumented.

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 clearly states the tool analyzes a test failure using AI, with a specific verb and resource. However, it does not distinguish itself from sibling tools like classify_failure or analyze_report, which also involve analysis of failures or reports.

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 provided on when to use this tool versus alternatives. The description gives no context about typical scenarios, prerequisites, or situations where another sibling tool would be more appropriate.

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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