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

Beyond the readOnly and idempotent annotations, the description reveals ranking behavior (citation/bill/docket first), lexical matching semantics, the result limit, output format, and the recommended follow-up fetch(id). This is rich behavioral context that substantially aids invocation.

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?

Every sentence earns its place: scope and output, ranking priority, matching behavior, query advice, and follow-up action. The description is front-loaded with the core purpose and contains no redundant text or 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?

The tool is simple, with one required parameter dump, and the description covers scope, constraints, output shape, ranking, and usage nuance. The output schema handles return details, so nothing critical is missing for an agent to call this tool correctly.

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?

With 0% schema description coverage, the description fully compensates by defining the accepted `collection` values (code, bills, opinions) and explaining how the `query` should be phrased for effective matching. This adds meaning well beyond the raw schema.

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 verb and scope ('Search every loaded collection...'), explains the optional collection filter, and specifies the output shape as up to ten {id, title, url} results. This clearly distinguishes the general search tool from collection-specific siblings like bills_search or code_search.

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 clear context: searches all collections by default or one via `collection`, and offers query-formulation guidance ('Ask in the words the document would use'). However, it does not explicitly name sibling tools as alternatives or state when not to use this tool, so the agent must infer that distinction.

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