Save hours every week. This recruit 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 burden of behavioral disclosure. It reveals nothing about what the tool actually does: no mention of sorting logic, ordering of results, whether the input is a pasted document, permissions, or output shape. Instead it advertises a "battle-tested prompt," which tells an agent nothing about runtime 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?
The text is short but entirely front-loaded with a marketing benefit ("Save hours every week") rather than the tool's action. Every sentence is filler or misleading domain framing, so none earns its place.
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?
A tool with a vague name, one undocumented purpose and no output schema needs a description that states the task and expected result. This one replaces that with sales copy, leaving an agent with no basis to decide whether to call it or how to interpret anything it returns.
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 coverage is 100% and there is only one optional parameter whose meaning is documented in the schema itself. Per the baseline rule for high coverage, 3 applies; the description adds no meaning beyond "Optional user query to scope the analysis" and offers no example of a valid query.
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?
"recruit sort tool" is only restated as "this recruit sort tool toolkit"; the description never states what is sorted, by what criteria, or what the output is. The one concrete claim, a "prompt and operating playbook for restaurant/brand growth," introduces an unrelated domain rather than clarifying the sort/triage function, so an agent cannot distinguish it from recruit-audit-tool or recruit-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?
There is no guidance on when to select this tool over the nine sibling recruit-* tools, no prerequisites, and no exclusions. "Save hours every week" and "works in any LLM chatbot" are promotional claims, not usage conditions.
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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