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

Annotations already declare readOnlyHint=true, so the tool's read-only nature is covered. The description adds the 'Senate LDA' scope and the intent-layer framing, which are useful conceptual details, but it doesn't disclose other behavioral traits such as rate limits, authentication requirements, or pagination behavior beyond the limit parameter. With annotations covering safety, a 3 is appropriate.

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 informative and front-loaded: the first sentence states the core purpose and data fields, then the bill= option, then the conceptual contrast, then concrete examples. Each sentence earns its place, though it's a bit longer than strictly necessary. It's well-structured for an agent scanning for key details.

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?

Given the tool has 5 optional parameters, no output schema, and no nested objects, the description provides a solid overview of what the tool returns (the various data points) and how to query it. It covers the main parameters and gives examples. It doesn't specify the return structure or clarify whether parameters can be combined, but for a data-lookup tool this is adequate.

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%, so each parameter (bill, limit, ticker, company, since_year) already has a clear description. The tool description adds example usage ('what is Coinbase lobbying on', 'who lobbied HR1234') that helps infer how parameters combine, but it doesn't add significant semantic detail beyond the schema. Baseline 3 is correct.

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 tool as Senate LDA lobbying disclosures and enumerates the exact data it returns (spending, firms, issues, agencies, bills, former government posts). It distinguishes itself from related tools by positioning lobbying as the 'intent layer' vs. super PAC money as the 'FOR' layer, making its purpose unambiguous.

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 gives a specific usage hint ('Or pass bill= for everyone who lobbied a given bill') and contrasts with super PAC tools ('This is the intent layer...'). It provides example queries like 'what is Coinbase lobbying on' to illustrate use cases, though it doesn't explicitly list when NOT to use it beyond the conceptual contrast.

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