Save hours every week. This checkup report decoder lite 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, yet it discloses nothing about what the tool actually does with the input, whether it mutates anything, what it returns, or any limits. 'Save hours every week' conveys zero behavioral information.
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 sentences of pure promotional filler with no operational content. Nothing is front-loaded because there is nothing substantive to front-load; every sentence would be better removed and replaced with 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 with no annotations and no output schema, the description must explain purpose, input handling, and expected result. It explains none of these, leaving the agent with only a marketing slogan and an ambiguous domain.
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 single 'query' parameter is fully described in the schema ('Optional user query to scope the analysis'), so the schema does the work. The description adds no meaning about how the query scopes the analysis, but with 100% coverage the baseline 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 description restates the name ('checkup report decoder lite toolkit') and pads it with marketing claims ('battle-tested prompt and operating playbook'). The one substantive clause, 'for restaurant/brand growth,' actually muddies what a 'checkup report decoder' does and conflicts with the name's medical-decoding connotation. An agent cannot tell what action this tool performs or how it differs from the sibling 'checkup-report-decoder'.
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 guidance, no prerequisites, and no mention of the non-lite sibling it should be chosen over. 'No setup, works in any LLM chatbot' is a sales claim, not a usage condition.
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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