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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description need not restate safety. It adds valuable non-obvious context: the data source (FHWA NBI June 2023), no auth required, public domain status, and the definition of 'Structurally Deficient' as Poor. This goes beyond the schema and annotations without contradiction.

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 longer than typical but each sentence adds meaningful context: purpose, output categories, FHWA alignment, SD definition, example stat, use cases, data source, and auth. The main verb and resource are front-loaded. It could be slightly trimmed, but it remains efficient and well-organized.

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 statistics tool with an output schema, the description covers all essential decision-making information: what it returns, the exact categories, data vintage, and accessibility. Nothing an agent needs to decide whether to call it or to interpret its high-level results 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?

Schema description coverage is 100% for the single parameter state_code, including format, examples, and leading-zero requirement. The description does not add parameter-specific guidance beyond the schema, so baseline 3 is appropriate.

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 ('Get') and resource ('aggregated bridge condition statistics for all highway bridges in a given US state'), and explicitly lists the output categories (Good, Fair, Poor, Unknown/Not-applicable). This clearly distinguishes it from sibling tools like bridge_detail or search, which focus on individual bridges or arbitrary bridge searches.

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 use cases ('state-level infrastructure health dashboards, policy analysis, and funding prioritization') and clearly scopes the tool to state-level aggregations, but it does not explicitly name sibling tools or state when not to use this tool. The absence of explicit exclusions is a minor gap, but the context is sufficient for an agent to infer appropriate usage.

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