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

Annotations already state that this is a safe, read-only, idempotent operation. The description adds useful behavioral context beyond that: include_cities adds the UI's latest-size-selected city matrix, and missing results remain null.

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 compact and front-loads the core scope. It is dense with domain-specific terms, but there is little obvious 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?

For a two-parameter read-only analytics tool with no output schema, the description covers the main data categories, the include_cities effect, and null handling. It does not fully explain election_id behavior, but the schema covers that parameter.

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 50%, with election_id well documented in the schema and include_cities left undocumented there. The description compensates by explaining that include_cities adds the city matrix and that missing results stay null, though the term 'latest-size-selected city matrix' remains somewhat opaque.

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 identifies a specific resource: national/locality aggregate analytics with fixed classifications, historical bloc changes, and medians. It is clearer than a tautology, but it does not explicitly distinguish this tool from siblings such as get_summary or get_demographics.

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 phrase 'National/locality aggregate analytics' implies the tool is for aggregate analytics rather than raw or detail-level queries. However, it gives no explicit when-to-use guidance, exclusions, or named alternatives among the many sibling tools.

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