Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It explains the semantic distinction between slot and measurement, states that the two are never summed or swapped, discloses that the tool measures rather than certifies, and clearly describes the UNREACHABLE state with the guarantee that no cached number is presented as live.

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?

The description is dense but not bloated. It front-loads the source and live nature of the data, then explains the two counts, their relationship, the dates, and the failure behavior. The phrasing 'We measure, never certify' is slightly stylistic, but the rest of the description earns its place by conveying important semantics.

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?

Given there is no output schema, no annotations, and zero parameters, the description is remarkably complete. It tells the agent exactly what values are returned, what they mean, how they differ, what freshness information is included, and what happens if the board cannot be fetched. No critical operational detail is missing for correct invocation and interpretation.

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 the description has no parameter semantics to add. It instead clarifies the meaning of the two numeric values it returns, which is the closest relevant semantic contribution. Baseline 4 is appropriate for a parameterless tool.

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 a specific verb and resource: 'Live GSPC board totals from https://councilof.ai/api/gspc.' It also specifies the exact return elements — slot count, measured count, and as_of dates — making it unmistakable what this tool does and how it differs from the sibling tools.

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 clearly implies when to use this tool: whenever a caller needs board totals presented live from the GSPC endpoint. However, it does not explicitly state when not to use it or compare itself to alternatives like list_cards or verify_card, so the usage guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.