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

Even with annotations declaring read-only/idempotent behavior, the description adds substantial context: the dual fast-path/grounded pipeline, the precise list of verdicts, the meaning of verification_error, and the crucial warning that could_not_verify must not be treated as evidence. This goes far beyond what annotations convey.

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 every sentence earns its place: trigger phrases, usage, processing paths, return values, caller warning, and efficiency benefit. It is well-structured, front-loaded with the most important info, and avoids redundancy with structured fields.

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 takes on the burden of explaining return values. It does so comprehensively, listing all verdicts, mentioning citations and reasoning, and distinguishing error states. It also covers the full scope of claims (company-financial and other), making it complete for a complex tool.

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 fully describes both parameters with examples and tolerance details (100% coverage), so the description does not need to repeat them. While the claim parameter is contextually illustrated in the description (e.g., company financial examples), no additional parameter-level semantics are provided beyond the schema, matching the baseline.

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 explicitly defines the tool as 'natural-language claim verification against authoritative sources' and provides trigger phrases like 'fact check' and 'verify the claim that…'. This clearly distinguishes it from sibling tools such as deep_research or ask_pipeworx by focusing on confirming/refuting specific factual claims.

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?

It states 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear usage context. It also breaks down the two major claim types (company-financial vs. other) and how each is handled, but 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.