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

Beyond annotations (readOnly, openWorld, idempotent), the description adds critical behavioral context: it explains the verdict types, the meaning of 'could_not_verify' (must not be treated as evidence), and 'unsupported' (no source covered). It also discloses the two processing pipelines and the return of citations. This prevents misuse and goes well beyond annotation hints.

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 longer than average but every sentence serves a purpose: defining the tool, giving usage triggers, outlining processing paths, specifying return values, and clarifying edge-case semantics. It is well-structured and front-loaded with the most important trigger phrases, making it easy to scan.

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 carries the burden of explaining return values, and it does so thoroughly (verdict list, actual value with citation, reasoning). It also covers error semantics, source fallback, and efficiency gains. For a tool of this complexity, the description is complete and self-sufficient.

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 value by explaining that tolerance_pct overrides the tolerance implied by the claim wording and gives a concrete use case (hallucination detection with 1–2%). This extra context justifies a 4, but not a 5 because the schema already documents both parameters clearly.

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 and resource: 'natural-language claim verification against authoritative sources.' It enumerates trigger phrases ('is it true...', 'fact check', etc.) and distinguishes between financial claims (via SEC EDGAR) and other factual claims (via grounded pipeline), which effectively separates it from sibling tools like ask_pipeworx.

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 an explicit when: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides trigger examples and explains that it replaces 4–6 sequential calls. However, it does not explicitly name alternatives or state when not to use it (e.g., for open-ended questions), so it falls short of a perfect 5.

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