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

Annotations already cover readOnly/idempotent/non-destructive, so the description goes beyond them by detailing the dual-pipeline behavior, the exact verdict list, and critical failure semantics. It explicitly warns that 'could_not_verify means the check did not happen' and 'must not be shown as one', and defines 'unsupported' as 'we looked and cover no source for it'—valuable context not inferable from annotations alone.

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 dense and front-loaded with the purpose and usage in the first two sentences, followed by pipeline details, return values, and critical caveats. It is somewhat long (about 200 words) but every sentence contributes functional information; the 'IMPORTANT' note is valuable. It could be tightened but is appropriately sized for the tool's complexity.

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?

Given no output schema, the description fully compensates by explicitly listing verdict types, the included citation, reasoning, and error semantics (verification_error{stage,detail}). It also explains the two execution paths and the consolidation benefit, covering the agent's need to select and interpret results without an output schema.

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 schema already provides full coverage (100%) with detailed descriptions for both 'claim' and 'tolerance_pct', including examples and defaults. The tool description adds little new parameter-specific meaning, only mentioning 'exact percent-delta math' which is not essential to parameter usage. Therefore the baseline 3 is appropriate; schema does the heavy lifting.

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 explicit natural-language triggers ("Is it true that…", "fact check") and clearly states the tool performs 'natural-language claim verification against authoritative sources'. It distinguishes itself by describing the two routing paths (SEC EDGAR/XBRL for company-financial claims vs. the grounded pipeline for anything else) and notes it 'Replaces 4–6 sequential calls', setting it apart from generic Q&A or research 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 gives a direct when-to-use instruction: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the internal routing logic for different claim types, implying broad applicability. However, it does not explicitly name sibling tools as alternatives or provide when-not-to-use scenarios, only that it consolidates multiple sequential calls.

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