Save hours every week. This story check 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 provided, so the description carries the full behavioral burden and largely fails it. It never states what the tool returns (a validation verdict? a rewritten story? a prompt?), whether the input is a story draft, or any constraints; worse, 'gives you a battle-tested prompt and playbook' implies a prompt-delivery tool rather than a checking operation, which actively misdirects.
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 ('Save hours every week') is pure advertising that delays any substantive information, and the remaining copy is generic boilerplate. Length is small yet nearly all of it is non-functional text.
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 is the only source of behavioral information, and it omits what the tool does, what it consumes, and what it returns. An agent could not reliably decide to call this over its siblings based on the definition alone.
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 only one parameter and schema description coverage is 100%, so the schema already documents 'query' as an optional scoping input. The description adds nothing about how the query scopes the check, but per the high-coverage baseline a 3 is appropriate.
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 text names the artifact ('story check tool toolkit') but never states the verb or outcome: it does not say it validates/reviews a user story against criteria, nor what a 'check' produces. The framing as 'a battle-tested prompt and operating playbook for restaurant/brand growth' is marketing positioning rather than a description of the operation, and it gives no signal to separate this from rd-story-gen-tool or rd-story-tpl-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?
'No setup, works in any LLM chatbot' is a portability claim, not usage guidance. There is no when-to-use, no exclusion, and no mention of the obvious alternatives in the rd-story-* family (gen vs check vs tpl), which an agent must disambiguate among.
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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