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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds interpretive context beyond annotations by explaining that silence indicates missing capture, not absence. This adds value without contradicting the annotations.

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?

Two sentences, front-loaded with the core concept 'Coverage map', followed by concrete examples and a usage directive. Every word earns its place; no filler or repetition.

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 zero-parameter read-only tool with no output schema, the description is complete. It covers what the tool does, the granularity (per-state, per-dataset), the contrast (federal vs local), and the interpretation of silence. No critical information is missing.

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 input schema has zero parameters, so the description cannot add parameter meaning. Per the rubric, 0 parameters yields a baseline score of 4. The description's examples of coverage dimensions (per-state, per-dataset) are not parameter-related but contextual.

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 'Coverage map: per-state, per-dataset completeness' with specific examples (officials by level, positions, finance, state-legislative votes). It distinguishes itself from siblings like politics_by_state by focusing on WHERE data is deep vs sparse, not on retrieving state data.

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 provides an explicit usage instruction: 'Use it to know whether silence means "no data captured" rather than "nothing there".' This clearly tells the agent when to invoke this tool, though it does not mention alternatives or when not to use it.

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