Save hours every week. This baby sleep planner 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 provided, so the description is the sole source of behavioral information, and it supplies none beyond generic sales language. It does not state whether this is read-only, what it produces, whether the optional query alters output scope, or any prerequisites. The restaurant-growth claim further misrepresents the tool's actual behavior.
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 every sentence is promotional filler that earns no place in a tool definition. Brevity does not compensate for content that is irrelevant to invocation; the space would be better used on one accurate sentence of purpose.
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 carry the entire burden, and it does not even identify the correct domain. An agent has no basis for predicting inputs, outputs, or safe usage; this looks like copy pasted from an unrelated brand/F&B tool.
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 schema already carries its meaning and the baseline is 3. The description adds nothing about how the query scopes results, but the coverage bar is already met elsewhere.
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 says this is a baby sleep planner. Instead it claims to be a 'prompt and operating playbook for restaurant/brand growth,' which is a different domain entirely. An agent reading this cannot tell what the tool does, and the name/title vs. description mismatch makes it actively misleading rather than merely vague.
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 is given. The only selection-adjacent claims are marketing lines ('Save hours every week', 'No setup, works in any LLM chatbot'), which convey no trigger conditions. The sibling tool 'baby-sleep-planner' is never mentioned, so nothing helps an agent choose between the lite and full variants.
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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