Save hours every week. This refund analyze 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 and fails it. It does not say whether the tool performs computation, retrieves a static template, or invokes an LLM prompt, nor what the caller receives back or what side effects (if any) occur.
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 promotional filler ("Save hours every week", "No setup, works in any LLM chatbot") rather than functional information. Brevity here comes from omitting substance, not from tight writing.
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 one param, no annotations, and no output schema, the description was the only place to explain behavior and it explains nothing. In a sibling set containing five other refund-* tools plus refund-negotiation, an agent has no basis to choose this one correctly.
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 coverage is 100% and there is a single optional parameter whose description ("Optional user query to scope the analysis") is fully documented in the schema. The prose adds nothing beyond that, so 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 ("This refund analyze tool toolkit") and then pivots to unrelated marketing language about "a battle-tested prompt and operating playbook for restaurant/brand growth." It never states what the tool actually analyzes about refunds, what inputs it consumes, or what it produces, so an agent cannot separate it from refund-diag-tool, refund-formula-tool, or refund-board-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?
"No setup, works in any LLM chatbot" is not usage guidance. There is no when-to-use condition, no exclusions, and no reference to any of the many refund-* siblings that overlap with this name.
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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