Save hours every week. This picky eater fix lite 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 disclosure burden, and it mostly spends that budget on promotion ('Save hours every week'). It hints that the output is a prompt plus an operating playbook, but says nothing about output shape, whether the query alters results, cost, 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?
It is short, but the lead sentence ('Save hours every week') is pure advertising that earns no place, and the remaining sentences are generic claims rather than the front-loaded statement of function an agent needs.
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 invoking this produces and how the prompt/playbook is returned; it does not. Combined with the domain mismatch between the name and the text, an agent lacks what it needs to invoke this 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?
There is a single optional 'query' parameter with 100% schema description coverage, so the schema already explains it. The description adds no meaning about how the query scopes the analysis, which is the baseline 3 case rather than a strength.
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 never states a verb or an operation; it advertises that the tool 'gives you a battle-tested prompt and operating playbook.' Worse, the resource is contradictory: the name says 'picky-eater-fix' (child feeding) while the text says 'restaurant/brand growth,' so an agent cannot tell what domain this actually serves. Marketing language substitutes for a statement of function.
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 exclusions, and no mention of the obvious sibling 'picky-eater-fix' (the non-lite variant) that an agent would need to choose between. 'No setup, works in any LLM chatbot' is a compatibility claim, not a selection rule.
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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