board_totals

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 exceeds expectations: it reveals the live-fetch nature, the slot-vs-measurement meaning, the never-summed/never-swapped constraint, both as_of timestamps, the trust posture ('We measure, never certify'), and the UNREACHABLE failure state with 'no cached number is ever presented as live'. This is genuinely transparent.

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 sentences, all load-bearing: the source resource, the data semantics with the integrity constraints, and the failure behavior. It is dense rather than padded, though it could be slightly trimmed without losing meaning. Front-loads the resource and purpose before the constraints.

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-parameter tool with no output schema, the description covers everything an agent needs: what is returned, the semantics of each number, the as_of timestamps, and the UNREACHABLE failure state. Nothing material is missing for correct invocation; a concrete return-shape example would be the only marginal addition.

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 there is nothing to document and the baseline of 4 applies. Rather than parameters, the description invests in documenting return semantics — labelled counts, kinds, and as_of dates — which is where the agent needs guidance given there is no output schema.

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?

States a specific verb and resource: 'Live GSPC board totals from https://councilof.ai/api/gspc... Returns the slot count and the measured count'. The distinction between slot and measurement together with 'never summed and never swapped' differentiates it clearly from siblings like get_axis, verify_card, and list_cards. An agent can tell this is the board-aggregate tool without ambiguity.

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 details what the tool returns and its integrity semantics, so when to use it is implied — it is the tool for live board totals. However, it gives no explicit routing against the named siblings and no when-not-to-use conditions. Behavior context is strong, but explicit usage exclusions are absent.

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