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

With no annotations and no output schema, the description carries the full disclosure burden and does real work: it reveals the exact count (18), that each issue has two sides, that wording is deliberately partisan per side, and that sides carry short labels. What it omits is the concrete return structure (field names, how sides are nested), which would matter for parsing.

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 no filler; the subject of the tool is front-loaded. The sentence about partisan wording earns its place by warning the agent how to read the content, though the phrasing 'the way its own supporters would say it' is slightly wordier than needed.

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 parameterless list tool with no output schema, the description supplies the essential shape of the data: cardinality, the two-sided structure, and the label vocabulary. Only the precise response schema is left unspecified, which is a minor gap given how simple the call is.

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 tool takes zero parameters, so the baseline is 4 and there is nothing to disambiguate. The mention of 'l' and 'r' describes output labels consumed by score_fit rather than input parameters, so it adds context without creating parameter confusion.

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 states a specific resource — the 18 issues tracked by Plain Ballot — so an agent knows this is the enumeration tool rather than get_ballot or score_fit. It stops just short of an explicit verb like 'returns' or 'lists', relying on the title to supply it, and it never contrasts itself with get_ballot.

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?

It tells the agent to use the short side labels when supplying priorities to score_fit, which implies a call-first relationship with that sibling, but that is guidance about downstream data use rather than an explicit 'call this before score_fit' instruction. No exclusions or alternate paths are stated.

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