Save hours every week. This policy write 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, the description carries the full burden of behavioral disclosure, and it discloses almost nothing: no indication of whether this mutates or stores anything, what it returns, what input it expects, or what the playbook/prompt actually does at invocation time. The only behavioral claims ("no setup", "works in any LLM chatbot") are packaging trivia, not operational context.
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 front-loaded line ("Save hours every week") is pure marketing and pushes the only semi-informative content to the back. Nothing in it earns its place for an agent deciding whether to call the tool.
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 policy-writing tool with no output schema and no annotations needs the description to explain what gets written, for which domain, and in what form. Instead the description is generic sales copy, leaving an agent unable to predict the result or distinguish it from a dozen sibling policy tools.
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% for the single optional "query" parameter, so the schema already documents it and the baseline of 3 applies. The description adds no meaning about how a query scopes the output, but it is not required to when the schema is fully described.
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 tool name ("policy write tool toolkit") and then pivots to promotional framing ("battle-tested prompt and operating playbook for restaurant/brand growth") rather than stating a concrete verb+resource action. An agent cannot tell what artifact this produces or how it differs from the many policy-* siblings such as policy-gen-tool, policy-rewrite-tool, or policy-polish-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 when-to-use guidance, no preconditions, and no mention of any alternative tool. "No setup, works in any LLM chatbot" describes packaging, not selection criteria, and the odd restaurant/brand-growth framing could actively steer an agent toward this tool for the wrong domain.
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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