FreeRateUpdate first-party dataset: the median loan amount our shoppers request in each state against that state median household income, as a ratio, by product. The sample floor is published with the figures so a thin state is visible rather than implied.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | Optional 2-letter state; omit for every state |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, closed-world behavior, so the description's job is to add nuance — and it does, disclosing that this is first-party shopper data and that a sample floor ships with the figures so thin states are visible rather than implied. That data-quality disclosure is genuinely beyond the annotations. It stops short of explaining how the ratio is computed or what happens for suppressed states.
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?
A single sentence, front-loaded with the dataset identity and then the metric definition, with no filler. The sample-floor note is the only embellishment and it carries real interpretive value.
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, rich annotations, and no output schema, the description supplies enough to call the tool and interpret results: metric definition, unit (ratio), geographic granularity, and a data-thinness caveat. The unexplained 'by product' dimension is the main remaining gap.
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 the single 'state' parameter is fully documented as an optional two-letter code with an omit-for-all convention. The description reinforces the state dimension but adds no format or filtering detail beyond the schema, 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 names a specific resource and computation: median requested loan amount versus median household income, expressed as a ratio, per state. It is distinguishable from siblings like get_state_property_tax or get_home_values. The 'by product' clause is not reflected in any parameter, leaving the granularity slightly ambiguous.
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?
It never says when to reach for this tool instead of related siblings such as get_home_values or get_mortgage_rates, nor what prerequisites apply. The only usage-shaped information is the sample-floor caveat, which is about interpreting results, not about tool selection.
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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