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

Annotations already declare readOnly and non-destructive, but the description adds crucial behavioral nuances: the meaning of could_not_verify (check did not happen, not evidence) vs unsupported (no source), the two verification pipelines, and the return structure with verdict, evidence, and citation. This goes well beyond the structured annotations and prevents serious misinterpretation.

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?

Though longer than typical, every sentence carries unique information: trigger phrases, use condition, dual-path detail, return values, edge-case warnings, and efficiency claim. The description is front-loaded with purpose and structured logically, with no filler or repetition of schema/annotations.

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?

Despite no output schema, the description enumerates all possible verdicts, explains the meaning of ambiguous outcomes, describes the evidence/citation format, and covers both financial and general claim handling. This is a complete picture for a complex tool, making it safe for an agent to invoke without additional discovery.

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 coverage is 100% with rich descriptions for both parameters, including examples and the default behavior for tolerance_pct. The main description does not add any parameter-specific details, so it neither enhances nor detracts from the schema. 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 opens with natural-language trigger phrases and clearly states the tool's function: 'natural-language claim verification against authoritative sources.' It specifies the resource (claims) and distinguishes itself from generic search tools by describing the two processing paths (structured SEC EDGAR for financial claims, grounded pipeline for others) and the verdict output. This is more than sufficient to differentiate from sibling tools.

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 explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides detailed operational contexts (financial vs other claims, when to set tolerance_pct for hallucination detection). It does not name sibling alternatives or explicitly state when not to use, but the guidance is clear and practical.

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