AI-powered restaurant & brand intelligence pack. Instantly generate a ready-to-use bug gen tool workflow - no prompt engineering needed. Ships as a full prompt kit with step-by-step instructions and a 30-day support promise.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional user query to scope the analysis. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full behavioral burden and mostly fails: it never says whether the tool is read-only or generative, whether it writes/creates anything, what permissions or setup it needs, or what the caller receives. The only concrete claims ('full prompt kit with step-by-step instructions', '30-day support promise') are commercial packaging terms rather than runtime behavior an agent can act on.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
It is short, but the front-loaded content is wasted on an unrelated 'restaurant & brand intelligence pack' framing and marketing promises instead of the tool's function. The most decision-relevant information (what gets generated, when to pick this over qt-bug-helper-tool) is absent entirely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and one undocumented-in-description parameter, the description must explain the deliverable and any side effects — it explains neither. An agent cannot determine from this definition what calling the tool will yield or whether it is safe to invoke.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single optional 'query' parameter is fully documented in the schema (100% coverage), so the schema does the explanatory work and a baseline 3 applies. The description adds nothing about what the query should contain or how it scopes the generation, so no credit above baseline is earned.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The only semi-functional clause is 'generate a ready-to-use bug gen tool workflow', which essentially restates the tool name ('qt-bug-gen-tool') in circular terms and never says what artifact is produced (bug report? test cases? bug-generation prompt template?). The lead sentence, 'AI-powered restaurant & brand intelligence pack', describes a completely different domain and actively confuses the agent about what resource this tool operates on.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No statement of when to use this tool, no prerequisites, and no mention of the obvious alternative sibling qt-bug-helper-tool (or qt-case-gen-tool / qt-script-gen-tool) that the agent must choose between. The 'no prompt engineering needed' line is a sales claim, not a usage condition, so the agent is left to guess.
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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