Save hours every week. This inspect check tool toolkit gives you a battle-tested prompt and operating playbook for restaurant/brand growth. No setup, works in any LLM chatbot.
| 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 disclosure burden, and it discloses almost nothing: no indication of whether the tool reads or mutates state, what it returns, or how the 'check' is performed. The single behavioral fact, 'no setup, works in any LLM chatbot', is incidental marketing rather than operational context.
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?
Only three short sentences, but they are front-loaded with a benefit claim ('Save hours every week') rather than the tool's function, and the remainder is promotional filler. Brevity here reflects under-specification, not tight writing.
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?
The interface is trivial (zero required parameters, no output schema), so the description could easily have been complete, yet it leaves the core question unanswered: what an inspection check produces and when to run it. Against a dense sibling namespace it gives an agent nothing to act on.
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?
Schema description coverage is 100% for the single optional 'query' parameter ('Optional user query to scope the analysis'), so the schema already does the work and the baseline of 3 applies. The description adds no extra meaning about how the query scopes the analysis or what happens when it is omitted.
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 description never states a concrete operation. "This inspect check tool toolkit gives you a battle-tested prompt and operating playbook" restates the name and adds marketing framing, not a verb+resource. An agent cannot tell from this text what the tool inspects or checks, nor how it differs from the sibling so-inspect-gen-tool.
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?
There is no when-to-use guidance, no preconditions, and no mention of any of the neighboring tools (so-inspect-gen-tool, so-alert-*, so-deploy-*) that an agent would need to choose between. "No setup, works in any LLM chatbot" is a selling point, not routing guidance.
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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