Save hours every week. This subtitle caption maker 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. It discloses only that no setup is required and it works in any chatbot; it says nothing about what the tool returns, what the optional query does, or what happens to the input. The 'prompt and operating playbook' phrasing hints at a static prompt-pack return but never states 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?
The text is short but front-loads pure marketing ('Save hours every week') rather than the tool's function, and 'battle-tested,' 'No setup,' and 'works in any LLM chatbot' are filler that earns no place. Nothing is wasted on detail, but what is there is not load-bearing.
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?
The tool itself is simple (one optional parameter, no output schema), which lowers the bar, but the description still fails to say what the tool produces or when an agent should pick it over the many adjacent copywriting/content tools. Marketing copy is not a substitute for a functional statement.
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 explains its meaning. The description adds nothing about how scoping the analysis changes output, so it stays at the baseline.
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 'subtitle-caption-maker' implies subtitle/caption generation, but the description spends its first sentence on 'Save hours every week' and then describes the tool as a 'toolkit' offering a 'battle-tested prompt and operating playbook.' No specific verb+resource is stated, and the 'restaurant/brand growth' framing conflicts with the caption-making implied by the name, leaving the actual function ambiguous.
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 or when-not-to-use guidance, and no sibling tool is named despite hundreds of adjacent content tools. 'No setup, works in any LLM chatbot' is a convenience claim, not a usage condition.
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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