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

With no annotations, the description carries the burden of disclosing non-obvious behavior, and it does state that the data is a booth-derived model, not an actual vote. This is a meaningful caveat that prevents an agent from presenting projections as real election results. It does not cover every limitation, but the key modeling caveat is present.

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 compact sentence that front-loads the core data ('projected first-preference shares per party') and then adds the key caveat. Every phrase earns its place, and there is 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?

For a one-parameter read-only data tool with an output schema, the description is largely complete: it specifies the metric, the aggregation level, and the modeled nature of the data. It is only missing explicit when-to-use guidance relative to sibling demographics tools, which is a minor gap given the simplicity of the tool.

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 schema has 0% description coverage, so the description should compensate for the single suburb_name parameter. The phrase 'the suburb's microburbs' implies the parameter identifies the suburb, and the parameter name itself is self-explanatory, but no explicit format or usage detail is provided.

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 as projected first-preference shares per party for a suburb and adds the crucial scope detail that it is aggregated across the suburb's microburbs. It lacks an explicit action verb like 'returns' or 'gets,' and it does not explicitly distinguish itself from sibling demographics tools, though no sibling appears to cover voting.

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 this is the tool to call when projected party vote shares for a suburb are needed. It does not name any alternative tools or state when not to use it, so usage guidance is only implicit rather than explicit.

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