Save hours every week. This persona formula 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 supplied, so the description carries the full disclosure burden. The phrase 'gives you a battle-tested prompt and operating playbook' hints that output is static prompt/playbook text rather than live analysis, but it never says what is returned, whether the query parameter alters the result, or what constraints exist.
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 sentence is a benefit slogan ('Save hours every week') rather than the function, and the second repeats the tool name. The valuable information, if any, is buried behind promotional framing.
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, no output schema, and a single optional parameter, the description is the only source of behavioral truth and it falls short: an agent cannot tell what invoking this produces or why it would pick it over persona-analyze-tool or persona-tpl-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?
There is a single optional 'query' parameter with full schema description coverage, so the schema already documents it. The description adds no meaning about how scoping works, which is why the baseline 3 applies rather than higher.
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 restates the tool name nearly verbatim ('persona formula tool toolkit') and never states a concrete verb or deliverable. The only substantive hint is 'prompt and operating playbook for restaurant/brand growth', which gestures at a domain but does not tell an agent what this returns or how it differs from persona-analyze-tool, persona-board-tool, persona-diag-tool, or persona-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?
'Save hours every week' and 'No setup, works in any LLM chatbot' are marketing claims, not usage guidance. There is no when-to-use condition, no exclusion, and no pointer to the sibling formula/analysis tools, so an agent has no basis for selecting this over the four other persona tools.
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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