Save hours every week. This complaint sort 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 behavioral burden, yet it does not disclose what the tool does internally, whether it mutates anything, what permissions or setup are needed, or what the response looks like. Claims such as "No setup, works in any LLM chatbot" are marketing statements rather than behavioral disclosure.
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 opening "Save hours every week" is marketing rather than a front-loaded purpose statement. The three sentences spend space on sales claims and compatibility instead of telling an agent what the tool does first.
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 tool has one optional parameter and no output schema, so the structured surface is simple. Still, the description does not explain what the tool returns or how to use the optional query, leaving an agent without enough context to invoke it confidently.
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 already explains that it scopes the analysis. The description adds no parameter meaning beyond that, but the baseline is 3 when the schema does the documentation work.
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 says it is a "complaint sort tool toolkit" and a "battle-tested prompt and operating playbook," but it never states what complaint-sorting actually does or what output an agent would get. It does not distinguish this from the many nearby complaint-* siblings such as complaint-summary-tool or complaint-audit-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?
It provides no when-to-use or when-not-to-use guidance, and no alternatives are named despite a large set of complaint-related sibling tools. The only contextual hint is "restaurant/brand growth," which is too broad to route an agent reliably.
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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