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

Annotations already indicate read-only, open-world, and idempotent behavior, but the description adds crucial nuance: the distinction between 'could_not_verify' (check did not happen, not evidence) and 'unsupported' (cover no source). It also details return values (verdict, actual value, citation, reasoning) and the fallback pipeline, which the annotations do not 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?

Although longer than typical, every sentence adds value: query examples, scope, routing logic, return types, and a critical caller caveat. It is front-loaded with common phrasings and purpose, and the structure is logically organized. No fluff or redundancy.

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 expected outcomes (verdict list, actual value, citation, reasoning) and explains failure modes. For a tool with two parameters and rich annotations, it covers all necessary context: when to use, how it routes, what results mean, and how to interpret edge cases.

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 description coverage is 100% with detailed descriptions for both parameters. The tool description adds no additional meaning beyond the schema; it mentions 'exact percent-delta math' but does not elaborate on tolerance_pct beyond what the schema already provides. Thus 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 clearly states a specific verb (validate) and resource (natural-language claim) against authoritative sources, and differentiates from siblings by explicitly covering two routing paths (SEC EDGAR for company-financial claims, grounded pipeline for everything else). It also distinguishes its scope (fact-checking) from general 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?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and explains how to categorize claims (company-financial vs. other). It also notes that it replaces 4-6 sequential calls, guiding the agent to prefer this tool over composing workflows.

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