Data coverage
aura_coverageWhich sports, seasons and data products Aura has right now, with measured completeness and freshness. Call when unsure whether Aura covers something.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
aura_coverageWhich sports, seasons and data products Aura has right now, with measured completeness and freshness. Call when unsure whether Aura covers something.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered without the description. The description adds that the answer includes measured completeness and freshness, which prevents treating ``covered/not covered'' as a bare boolean and is a useful behavioral nuance. It does not describe the shape of the response, but with no output schema and a very simple read, that is a modest gap.
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?
Two sentences cover what it returns and when to call it, with the result content front-loaded. There is no filler, repetition, or restating of the tool name.
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 zero-argument, read-only discovery tool with full annotation coverage, the description gives enough to call confidently. Since no output schema exists, a brief hint about the return shape — for example, a list versus a nested breakdown — would have closed the only 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?
The tool takes no parameters, so the baseline of 4 applies; there is nothing a description could add about argument semantics 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 names the exact resource it enumerates — sports, seasons and data products currently available in Aura — plus the quality dimensions (completeness, freshness). That is clearly distinguishable from every sibling, which all fetch concrete entities or games. It is a noun phrase rather than an explicit verb+object, which keeps it just short of a 5.
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?
"Call when unsure whether Aura has a given sport, season, or product" states an explicit trigger condition for the call. No when-not-to-use or named alternative is given, but for a discovery tool whose only alternative is guessing, that omission is minor.
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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