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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond that: results are about resolved races, adjusted for race difficulty, and raw scores would mislead. This is meaningful context, even though return-shape details are left to the output schema.

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 short sentences with each one serving a distinct purpose: describe the ranking, state the use case, and warn about the difficulty adjustment. There is no filler or redundant restatement 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?

With only two optional parameters, complete schema documentation, and annotations covering behavioral guarantees, the description gives an agent everything needed to select and invoke the tool correctly. The trust-framing and difficulty caveat strengthen contextual completeness.

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 description coverage is 100%, and both 'limit' and 'office' already have clear descriptions in the input schema. The tool description does not add parameter-specific meaning beyond that, so the baseline of 3 applies.

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 clearly identifies the resource ('election forecaster, model, prediction market and pollster') and scope ('resolved races'), and ties it to an applied question ('whose election forecast should I trust'). It is readily distinguishable from sibling tools like forecast_race, which focuses on individual race forecasts.

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 an explicit use case: 'Use this to answer whose election forecast should I trust.' It also provides a practical caveat about difficulty adjustment. However, it does not name sibling alternatives or state explicit when-not-to-use conditions.

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