FEC Election Totals

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

Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds value by detailing the specific aggregate data returned (receipts, disbursements, count), which goes beyond the annotations. No contradictions.

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 a single, front-loaded sentence of 12 words that clearly states the purpose and data scope. Every word is meaningful, no filler.

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 the presence of an output schema and the tool's simplicity, the description adequately explains what data is returned. It could mention that all parameters are optional, but the schema already implies that. The description is complete enough for an agent to understand the tool's functionality.

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 all parameters are well described in the input schema. The description mentions 'by cycle, office, and state' but does not add new semantic meaning beyond what the schema provides.

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 starts with a specific verb ('Aggregate') and resource ('election totals'), and clearly specifies the grouping dimensions (cycle, office, state) and the data fields returned (receipts, disbursements, candidate count). This distinguishes it from sibling FEC tools like candidate details or committee totals.

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 implies usage for high-level aggregate totals but does not explicitly state when to use this tool versus alternatives like gov.fec.candidates or gov.fec.committee_totals. No 'when not to use' guidance is provided.

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