Save hours every week. This store area insight 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 provided, so the description carries the full behavioral burden, and it discloses almost nothing: no indication of what the call returns (a prompt? a playbook? an analysis?), whether it reads or mutates any data, or how the optional query changes behavior. "Save hours every week" and "battle-tested" are marketing claims, not behavioral facts an agent can act 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 that are almost entirely promotional ("Save hours every week," "battle-tested," "No setup"), consuming the limited space without delivering selection or invocation value. The length is fine, but the content is wasted on hype rather than front-loading purpose.
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 should explain what the agent gets back and how to use the query argument. Instead it stops at a sales pitch, leaving the actual capability and return shape undefined, which is inadequate even for a one-parameter tool.
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 parameter with 100% schema description coverage ("Optional user query to scope the analysis"), so the schema already does the work and the baseline is 3. The description adds nothing about how the query scopes the analysis or what form it should take, but no compensation is required at full coverage.
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?
"Store area insight toolkit gives you a battle-tested prompt and operating playbook" restates the name in marketing terms without a concrete verb or resource: it never says whether it analyzes store-area data, generates a prompt, or returns a playbook. The only domain hint is "restaurant/brand growth," which is too broad to distinguish it from siblings like fnb-store-diagnostic or store-site-selector.
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 when-to-use guidance, no prerequisites, and no named alternative among the many store/fnb siblings. The phrase "No setup, works in any LLM chatbot" describes convenience, not the conditions under which an agent should pick this tool over restaurant-ops-diagnostic or store-traffic-plan.
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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