Save hours every week. This review 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?
With no annotations supplied, the description carries the full burden and discloses nothing: it never says whether the tool reads existing reviews, generates a checklist, scores, or mutates data, nor anything about permissions, side effects, or output. The only behavioral claim ("battle-tested prompt") describes packaging, not behavior.
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?
Three short sentences, but they are front-loaded with a benefit claim ("Save hours every week") instead of the tool's function. Nearly every sentence is promotional filler that does not earn its place in a tool definition.
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?
For a tool with zero annotations, no output schema, and one undocumented parameter, the description should explain what the tool does with the query and what an agent gets back. It instead delivers advertising copy, leaving an agent with no usable operational information.
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, so the schema supplies baseline meaning. However, the description never mentions the parameter or how a scoping query changes results, and its framing ("works in any LLM chatbot") suggests the parameter is irrelevant — slightly below the 3 baseline when schema coverage is high.
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 restates the name ("review check tool toolkit") and surrounds it with marketing language ("battle-tested prompt and operating playbook", "Save hours every week") rather than stating a concrete verb+resource. Only the phrase "restaurant/brand growth" hints at domain, and it never clarifies what a "review check" produces, nor distinguishes it from siblings like review-audit-tool, review-sort-tool, or review-summary-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?
No when-to-use, when-not-to-use, or alternative-tool guidance appears anywhere. "No setup, works in any LLM chatbot" is a delivery claim, not a selection criterion, so an agent has no basis for choosing this over the many adjacent review-* tools.
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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