Save hours every week. This stock entry guide 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, yet it discloses almost nothing: it does not say whether invoking it returns a static prompt template, runs an analysis, or performs any mutation. The only behavioral hint is that it yields a 'prompt and operating playbook', which implies a text-returning, read-only operation but is never stated explicitly.
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 they are front-loaded with a benefit slogan ('Save hours every week') rather than the tool's function. The space is spent on marketing claims ('battle-tested', 'No setup') that give an agent no selection signal, so the text does not 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 must explain what is returned and how the tool differs from the dense field of stock-* and restaurant-* siblings; it does neither. An agent reading only this definition cannot confidently decide to call it or predict the result.
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 of 3 applies. The description adds no meaning about how the query scopes the output, 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 name promises a 'stock entry guide', but the description never states what the tool actually does when invoked, only that it 'gives you a battle-tested prompt and operating playbook for restaurant/brand growth'. 'Stock entry' is never defined (inventory entry? stock-market entry? restaurant supply?), so the resource is ambiguous and indistinguishable from siblings like stock-analyze-tool or brand-fnb-playbook.
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 statement, no exclusions, and no named alternative among the many stock-* and restaurant-* siblings. 'No setup, works in any LLM chatbot' is a deployment claim, not guidance on when an agent should select this tool over another.
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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