Save hours every week. This recruit gen 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 exist, so the description carries the full behavioral burden, and it fails: it never says what the tool outputs (a prompt? a playbook? a recruiting document?), whether it mutates anything, or what a call returns. The only semi-substantive claim is that it delivers "a prompt and operating playbook," which hints at an output shape but not a reliable one.
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 the lead is a benefit slogan ("Save hours every week") rather than the tool's function, and the rest is promotional filler. The brevity comes from omission, not from efficient specification.
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?
No annotations and no output schema mean the description must explain the deliverable and call semantics, and it does neither. For a simple one-parameter tool the bar is low, but an agent still cannot predict what a call returns or when to prefer it over a sibling.
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?
With a single optional parameter and 100% schema description coverage, the schema already documents "query." The description adds no meaning about how the query scopes the generated content, so the baseline of 3 applies.
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 text restates the name ("recruit gen tool toolkit") and pivots to marketing claims about "restaurant/brand growth" rather than stating what the tool actually generates. Nothing distinguishes it from the many recruit-* siblings such as recruit-write-tool, recruit-audit-tool, or recruit-tpl-tool. An agent cannot tell what artifact this produces.
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 prerequisites, and no mention of any alternative tool. "No setup, works in any LLM chatbot" describes packaging, not the selection condition. The agent is left to guess entirely.
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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