get_usageHow many bet checks the caller has left and when the next one frees up - 100 a day shared per device anonymously, 1000 an hour with a signed-in user's token. Free, spends no check. It grades nothing, so it is not a step before a check: call it when a check comes back refused for quota, or before working through a long list of bets.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description discloses meaningful behavioral details: the operation is free and 'spends no check,' and it spells out rate limits (100/day anonymously, 1000/hour with a signed-in token). These details are not available from annotations and materially inform how an agent should plan calls.
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 dense sentences front-load the core answer ('How many bet checks...'), then add quota mechanics, cost behavior, and usage guidance without redundancy. Every clause earns its place.
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 parameterless read-only quota query with a readOnlyHint annotation and no output schema, the description fully covers what an agent needs: the meaning, the quota limits, the free/no-check behavior, and when to invoke it. Nothing important is missing.
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?
The tool has zero parameters, so schema coverage is 100% by definition. The baseline for zero parameters is 4; the description correctly focuses on behavior and usage rather than inventing parameter details.
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 states exactly what the tool does: reports how many bet checks the caller has left and when the next one frees up, with specific quota numbers. It also explicitly differentiates from the sibling check_bet by stating 'It grades nothing', making its role unambiguous.
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 description gives explicit when-to-use guidance: 'call it when a check comes back refused for quota, or before working through a long list of bets.' It also states a clear when-not-to-use: 'not a step before a check,' which helps an agent route to check_bet instead when grading is needed.
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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Each tool has a completely distinct purpose: check_bet grades a specific bet, get_slate checks game availability, and get_usage tracks quota consumption. There is no overlap or ambiguity between them, and the descriptions reinforce their separation clearly.
All tool names follow a consistent verb_noun pattern: check_bet, get_slate, get_usage. This makes the API predictable and easy to navigate, and there are no mixed conventions or vague verbs.
Three tools is a very appropriate size for this focused betting-checker service. Each tool plays an essential role in the workflow (pre-check slate, grade bet, manage quota) without any redundancy or bloat.
The tool surface covers the full lifecycle needed for the domain: checking if a slate is available, grading a bet, and monitoring usage limits. There are no obvious gaps—the service is self-contained for its stated purpose.