Save hours every week. This doc tpl 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 burden of behavioral disclosure. It never explains what the tool returns, whether it generates content or just returns a static prompt, whether the query parameter influences the output, or what format the playbook takes. The only behavioral hint is the no-setup claim, which is thin.
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 description has three sentences, and the first one, 'Save hours every week', is pure sales copy that adds no information. 'Doc tpl tool toolkit' is redundant padding, and the remaining content is vague marketing language rather than a front-loaded explanation of what the tool does.
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?
For a tool whose only distinguishing job is delivering a template or prompt artifact, the description fails to say what the artifact contains, how it is returned, or how the query parameter scopes it. The lack of annotations and lack of output schema make this omission more serious.
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?
The tool has one optional parameter and schema description coverage is 100%, so the schema already documents what 'query' does. The description adds no meaning beyond that, but because the schema does the work, the baseline score of 3 applies.
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 is only restated as 'doc tpl tool toolkit', and the actual purpose ('prompt and operating playbook for restaurant/brand growth') is buried in marketing copy. There is no specific verb or resource, and nothing distinguishes it from dt-doc-gen-tool or dt-doc-check-tool, which appear in the sibling list.
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?
The description gives no indication of when to use this tool, when to choose it over dt-doc-check-tool or dt-doc-gen-tool, or what the optional query parameter does. 'No setup, works in any LLM chatbot' is not usage guidance.
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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