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

Annotations already declare read-only, idempotent, and non-destructive behavior, and the description adds significant behavioral context: internal pipeline (SEC EDGAR + XBRL fast path, grounded fallback), verdict types, and the crucial clarification that could_not_verify means the check did not happen and must not be treated as evidence. This is valuable beyond the annotations.

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 front-loaded with purpose and query phrasings. Each sentence carries meaningful operational information (pipeline steps, verdict semantics, caller warning, efficiency comparison). It could arguably be tightened, but the length is justified by the tool's complexity and there is no filler. Structure is logical and clear.

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?

With no output schema, the description fully specifies the return structure (verdicts, actual value with citation, reasoning) and elaborates on edge cases (could_not_verify with verification_error, unsupported). Combined with the rich parameter schema and annotations (read-only, idempotent), the description leaves no major operational gap for an agent to select and invoke the 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?

Schema coverage is 100%: both claim and tolerance_pct have detailed descriptions with examples and defaults. The tool description does not add parameter-specific meaning beyond the schema; it mentions 'exact percent-delta math' in passing but that is more behavioral than parameter guidance. Baseline 3 is appropriate due to high schema coverage.

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 states the tool's purpose: natural-language claim verification against authoritative sources. It lists specific query phrasings ('Is it true that...', 'fact check', 'verify the claim that...') and explicitly says to use it when checking if something a user said is factually correct. It also distinguishes itself from siblings by noting it replaces 4–6 sequential calls and handles two distinct pathways (structured SEC EDGAR vs grounded fallback), making it uniquely suited for fact-checking.

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 explicit context: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also details the two scenarios (company-financial claims via SEC EDGAR, any other claim via grounded pipeline). However, it does not explicitly name alternative tools or state when not to use it, so it falls 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.