Save hours every week. This sanhao fnb roi gate 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: not whether invocation is read-only, whether it returns a static prompt/playbook versus a computed analysis, whether it uses the 'query' to scope output, or whether results are deterministic. The only behavioral hint is that the deliverable is a 'prompt and operating playbook', which is thin for an unannotated tool.
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 it is front-loaded with a benefit slogan ('Save hours every week') instead of the function, and every sentence is promotional rather than informative. No sentence tells the agent what the tool does or when to call it, so brevity here reflects omission rather than economy.
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?
The tool has one optional param, no output schema, and no annotations, so the description must explain both what the ROI gate computes and what form the output takes. It conveys only a broad domain ('restaurant/brand growth') and that a prompt/playbook is the artifact, leaving the actual gate logic, scope, and expected result undocumented.
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?
With a single optional parameter and 100% schema description coverage ('Optional user query to scope the analysis'), the schema already documents the parameter. The description adds no syntax, format, or example of what a good query looks like, 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 description mostly restates the name with marketing framing: it says the tool 'gives you a battle-tested prompt and operating playbook for restaurant/brand growth' but never explains what an 'ROI gate' evaluates, what decision it gates, or what the caller gets back. It names no sibling to distinguish from, which is critical given near-duplicates like 'fnb-roi-gate', 'roi-analyze-tool', 'roi-diag-tool', 'chain-mkt-roi' and 'fnb-monthly-roi-report'.
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, when-not-to-use, or prerequisite information. The only contextual claims ('Save hours every week', 'No setup, works in any LLM chatbot') are ad copy rather than routing guidance, so an agent has no basis for choosing this over the many other F&B/ROI siblings.
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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