Save hours every week. This cvr 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 behavioral burden, and it discloses almost nothing beyond "no setup." It does not say whether the tool only returns prompt/playbook text, whether it performs any analysis, whether it has side effects, or what the response looks like. Calling it a "battle-tested prompt and operating playbook" hints at a static text deliverable but is too vague for an agent to rely on.
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 two of them are pure promotional filler ("Save hours every week," "No setup, works in any LLM chatbot") that consume the prime front-loaded position without conveying capability. The one substantive clause about restaurant/brand growth is buried last, so the structure actively works against comprehension.
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 heavily populated sibling namespace, the description should explain what a "cvr formula" artifact is and how it relates to the analyze/board/diag/tpl variants. Instead it repeats a sales pitch, leaving the agent unable to judge fit or expected output.
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 documents its meaning; baseline 3 applies. The description does not add any scoping or format guidance for "query" (e.g., what kinds of queries are useful), but it does not mislead either.
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 restates the name ("This cvr formula tool toolkit") and layers on marketing claims rather than a verb+resource statement. It surfaces a domain ("restaurant/brand growth"), but never says what the tool actually produces (a formula? a prompt template? a calculation?) or how it differs from cvr-analyze-tool, cvr-diag-tool, or cvr-tpl-tool. An agent cannot tell what calling this does.
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, when-not-to-use, or alternative-routing guidance anywhere in the description. The only quasi-contextual line, "works in any LLM chatbot," describes deployment, not selection criteria, and gives no hint about which of the many cvr-*/formula-* siblings to pick.
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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