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

Beyond the annotations (read-only, open-world, idempotent), the description discloses important behavior: automatic routing to structured vs. grounded pipeline, verdict semantics, the crucial distinction between 'could_not_verify' (pipeline failure) and 'unsupported' (no source coverage), and the performance benefit of replacing sequential calls. This is rich, non-obvious context.

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 long but dense and front-loaded with query examples and a clear use case. The 'IMPORTANT for callers' section is valuable, though the single-paragraph structure could benefit from bullets or breathing room. Still, every sentence earns its place.

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 complex tool with no output schema, the description adequately explains return values (verdict list, actual value, citation, reasoning), error semantics, routing behavior, and cancellation of multi-call sequences. It gives enough context to correctly interpret results and avoid misuse.

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 already documents both parameters thoroughly with 100% coverage. The description adds context about percent-delta math and tolerance, but it mostly reinforces what the schema states rather than adding new parameter-level semantics, so the 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 purpose is explicit: natural-language claim verification against authoritative sources, with concrete query examples and outcome types. It clearly distinguishes this from general research/grounded-answer tools by focusing on fact-checking of user statements, even if sibling tools are not named.

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 clear trigger: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains how company-financial claims are routed vs. other claims, but doesn't explicitly state when NOT to use it or name alternative tools, so it stops short of a 5.

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