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QA Copilot AI

QA Copilot AI

by Sham-coder16
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

summarize_bug

Generate structured, professional bug reports from any bug description. Turn raw issue details into clear, actionable summaries for QA teams.

Instructions

Generate professional bug reports 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": {
      "bugDetails": "<bugDetails>"
    },
    "name": "summarize_bug"
  }
}

Input Schema

NameRequiredDescriptionDefault
bugDetailsYesBug description or issue

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure, but it only states that it 'generates' reports. It does not disclose whether the operation is read-only, whether external AI services are invoked, or what the output format might be. This is a significant gap for a generative tool.

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, efficient sentence that is front-loaded with the action. The phrase 'using AI' is somewhat redundant filler, but it does not materially hurt clarity or structure.

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

Completeness4/5

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

Given the low complexity (one parameter, no nested objects, no output schema), the description sufficiently conveys the tool's purpose and the input. However, the lack of output format details and sibling differentiation prevents a perfect score.

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 fully covers the single parameter (bugDetails) with a clear description. The tool description adds no extra param semantics, so the baseline 3 is appropriate since the schema already provides sufficient meaning.

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's function: generating professional bug reports. It uses a specific verb ('Generate') and resource ('bug reports'), but it does not differentiate from sibling tools like analyze_report or classify_failure, which could also relate to bug report generation.

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention scenarios, prerequisites, or exclusions, leaving the agent to infer usage solely from the tool name and siblings.

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

Deploy Server

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