Save hours every week. This policy 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 provided, so the description carries the full behavioral burden, but it only uses marketing language like "battle-tested prompt and operating playbook." It does not state whether the tool is read-only, what checks it performs, what output it produces, or any permissions or side effects.
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 description is short, but it is front-loaded with unverifiable marketing claims ("Save hours every week") rather than the concrete purpose. The sentences do not earn their place because they fail to explain what the tool checks or how to use it.
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 policy-checking tool with no annotations and no output schema, the description should explain the checking behavior, expected output, and relevant scope. None of that is present, leaving the agent unable to understand what the tool does or how to interpret its results.
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?
The schema has 100% description coverage for the single optional query parameter, so the schema already explains that it scopes the analysis. The description adds no additional parameter meaning, making the baseline 3 appropriate.
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 phrase "policy check tool toolkit" largely restates the tool name, and "restaurant/brand growth" does not specify what policy domain is checked or what the tool actually returns. It does not distinguish itself from many sibling policy-* tools such as policy-audit-tool, policy-summary-tool, or policy-write-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?
The description gives no when-to-use guidance, no preconditions, and no alternatives. "Works in any LLM chatbot" is not actionable routing guidance for choosing this tool over the many sibling policy 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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