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

No annotations exist, so the description carries the full burden. It discloses critical behavioral details: live_results has one entry per contest, closed primaries return parties separately, vote_pct is within-party, and warns against cross-contest comparisons. It also instructs to never call or project races and to report counts exactly, which is essential for correct data interpretation.

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?

Every sentence serves a purpose: purpose, data provenance, data interpretation rules, usage, and behavioral constraints. It is front-loaded with the core purpose and structured logically. No fluff or redundancy; dense but efficient.

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 no annotations and no output schema, the description must cover key usage nuances. It does so effectively, explaining the per-contest structure and vote_pct semantics. It does not explicitly mention pagination or parameter filtering, but those are fully described in the schema, so the description is sufficiently complete for correct invocation.

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% for all 7 parameters, so the baseline is 3. The description adds no parameter-specific meaning beyond the schema, but it does explain the data semantics of the result, which indirectly helps parameter use. No additional compensation needed given full schema coverage.

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 states the tool returns the 2026 midterm election slate: races, candidates, and ballot measures per state, with official results. It distinguishes from siblings like get_candidate (single candidate) and list_candidates (candidate list) by specifying the aggregated election-slate scope.

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?

Explicitly says 'Use for who is running for X and results questions,' giving a clear usage context. It does not explicitly list exclusions or alternative tools, but the guidance is sufficient to route the agent correctly among the many election-related siblings.

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