Save hours every week. This skill review clock 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 burden. It says nothing about what the tool actually does at runtime, whether it produces a prompt, a review, or a document, whether anything is persisted, or how output is shaped. The only behavioral claims ("no setup", "works in any LLM chatbot") are promotional rather than operational.
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?
It is short, but every sentence is marketing copy ("Save hours every week", "battle-tested") rather than information an agent can act on. Nothing useful is front-loaded because nothing substantive is present.
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 no behavioral detail, an agent cannot determine what this tool returns or how it differs from the large set of brand/fnb siblings. The definition is effectively incomplete for invocation decisions.
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 100% schema description coverage, so the schema already explains it. The description adds no meaning about how the query scopes the analysis, so the baseline 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 text largely restates the name ("skill review clock toolkit") and dresses it in marketing language ("battle-tested prompt and operating playbook for restaurant/brand growth"). It never states the concrete action the tool performs or what the agent gets back, and with hundreds of similar fnb-/brand- siblings there is no differentiation.
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 sales claim, not usage guidance. There is no statement of when to pick this over the many restaurant/brand siblings, no prerequisites, and no exclusions.
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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