Save hours every week. This fnb monthly roi report 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 disclosure burden, and it delivers none: nothing about what the tool returns, whether it is a read-only analysis or a generation step, whether any state is created, or what happens with an empty query. "Save hours every week" is marketing benefit language, not behavioral information 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?
It is short, but every sentence is promotional filler ("Save hours every week," "battle-tested," "No setup") and none is front-loaded with the tool's function. The space is spent on sales copy rather than an operation statement, so brevity does not translate into efficiency.
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 no output schema, no annotations, and a single loosely-specified optional parameter, the description needed to explain what a "monthly ROI report" contains and how results are shaped. Instead it describes a generic prompt pack, leaving the agent unable to predict the response or judge whether the call is appropriate.
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, so the schema already explains the `query` field. The tool description adds no further meaning about how a query scopes the analysis or what form it should take. Per the high-coverage baseline, a 3 is the correct ceiling here.
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 tool name ("fnb monthly roi report toolkit") and pads it with promo copy ("battle-tested prompt and operating playbook for restaurant/brand growth"). It never states a verb or what the tool actually produces — no mention of computing ROI, generating a monthly report, or what inputs yield what output. Among dozens of roi-* and fnb-* siblings, an agent has no basis to pick this one.
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?
The only usage-adjacent statements are "No setup, works in any LLM chatbot," which describe deployment convenience rather than when to invoke this tool. No condition, no exclusions, and no reference to close alternatives such as roi-analyze-tool, fnb-roi-gate, sanhao-fnb-roi-gate, or chain-mkt-roi, all of which overlap in intent.
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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