Save hours every week. This anomaly tpl 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 burden, and it discloses almost nothing behavioral: not whether it analyzes data, returns a template, or requires an existing anomaly report. "No setup" is the only behavioral claim and it is too vague to plan around. No mention of output shape, data requirements, or side effects.
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 nearly all of the text is promotional ("Save hours every week," "battle-tested," "No setup") rather than functional. The most useful fragment, the restaurant/brand growth template purpose, is buried behind sloganeering instead of front-loaded.
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 optional parameter, no annotations, no output schema, and four closely named siblings, the description should at minimum state what artifact is produced and how it differs from the analyze/diag/formula/board variants. It does neither, so an agent cannot confidently select or invoke it.
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% and there is a single optional 'query' parameter already documented in the schema as scoping the analysis. The description adds no meaning beyond that, but with full schema coverage 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 description mostly restates the name ("anomaly tpl tool toolkit") and wraps it in marketing language. The only substantive claim is that it delivers "a battle-tested prompt and operating playbook for restaurant/brand growth," which hints at a template artifact but never states the specific action or output. It gives no signal distinguishing it from siblings like anomaly-analyze-tool, anomaly-diag-tool, anomaly-formula-tool, or anomaly-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?
There is no guidance on when to pick this tool over the four sibling anomaly-* tools, nor any stated prerequisites or inputs. "Works in any LLM chatbot" is a compatibility claim, not usage guidance. The agent is left to infer the selection rule entirely.
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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