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

The description goes far beyond the annotations. It discloses the internal routing logic, the exact verdict types returned, the presence of citations, and the crucial error semantics (could_not_verify means the check did not happen and must not be shown as evidence). This aligns with the readOnlyHint and idempotentHint annotations without contradicting them.

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 well-structured. It starts with natural-language examples, then defines purpose, explains internal paths, and covers return values and error handling. Every section serves a purpose, though some phrases (e.g., the list of example phrasings) could be trimmed without losing essential information.

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?

There is no output schema, so the description fully specifies the return shape (verdict, value, citation, reasoning) and error modes. It also covers both structured and grounded claim paths, making the tool self-contained and reducing the need for the agent to infer behavior from the schema or annotations alone.

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%, so the baseline is 3. The description adds meaningful nuance for tolerance_pct by explaining it overrides the claim-wording implied tolerance and suggesting 1-2% for hallucination detection. The claim parameter is already well-described in the schema, so the added value is moderate but useful.

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 uses specific verbs ('validate', 'verify', 'fact check') and clearly identifies the resource (natural-language claims against authoritative sources). It distinguishes itself from sibling tools like ask_pipeworx and deep_research by focusing on claim verification, and even notes that it replaces 4-6 sequential calls.

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?

Explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the two internal paths (SEC EDGAR/XBRL for company-financial claims, grounded pipeline for others), and clarifies the meaning of could_not_verify vs unsupported, giving the agent clear guidance on how to interpret and use the result.

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