Save hours every week. This report tpl 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 behavioral burden, yet it discloses nothing about what invoking the tool does, what the 'playbook' output looks like, whether the query parameter changes the result, or what any side effects might be. The marketing copy actively obscures behavior rather than revealing it.
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 sentence ('Save hours every week') is pure promotional filler that occupies the most valuable front-loaded position. Nothing substantive follows it, so the structure is efficient in length but empty in content.
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, no output schema, and an entirely promotional description, an agent has essentially no information needed to call this tool correctly. Even a simple template utility needs a statement of what the template generates and when it applies, and none is present.
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% with a single optional parameter, so the schema already documents 'query' fully, making 3 the baseline. The description adds no additional meaning about how the optional query scopes the analysis, so it neither helps nor misleads here.
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 name ('report tpl tool toolkit') and adds only marketing phrasing ('battle-tested prompt and operating playbook') rather than a concrete verb+resource statement. It does hint at a domain (restaurant/brand growth) and at being a template, but it never says what the tool actually produces or how it differs from siblings like report-analyze-tool, report-board-tool, or report-formula-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 exclusion criteria, and no mention of alternatives among the closely named report-* siblings. 'No setup, works in any LLM chatbot' is a selling point, not usage guidance, leaving an agent with no basis for selecting this tool over the many adjacent ones.
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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