Save hours every week. This monthly 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure and delivers none. It says nothing about whether the tool generates, mutates, or stores anything, what the response looks like, or whether the "prompt and playbook" is returned as text. "No setup, works in any LLM chatbot" is a sales claim, not behavioral 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?
The text is short but entirely promotional — "Save hours every week" and "No setup, works in any LLM chatbot" consume the front-loaded position that should carry the actual purpose. Not a word count problem; every sentence fails to earn 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?
With no annotations and no output schema, the description is the only source of behavioral information, and it omits what the tool returns, what format, and how the optional query affects results. The only usable signal is the restaurant/brand-growth domain, which is insufficient to call the tool correctly.
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 baseline is 3. The description adds no meaning about how a query scopes the output or what happens when it is omitted, but the schema already documents the parameter adequately.
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 restates the tool name ("This monthly write tool toolkit") and adds only marketing claims about a "battle-tested prompt and operating playbook for restaurant/brand growth." It never states a concrete verb+resource — what the tool actually produces (a monthly report? a monthly content plan?) is left ambiguous, so an agent cannot distinguish it from monthly-gen-tool, monthly-write-tool's siblings, or monthly-tpl-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?
No when-to-use guidance at all. With dozens of monthly-* siblings (monthly-audit-tool, monthly-check-tool, monthly-gen-tool, monthly-summary-tool, monthly-polish-tool), the description offers no routing rule, no prerequisites, and no exclusions.
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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