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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description discloses critical behavioral nuances: the verdict vocabulary, the semantic difference between could_not_verify (check did not happen, carries verification_error, not evidence) and unsupported (no source covers it), and the two routing paths (structured SEC EDGAR vs. grounded pipeline). This adds substantial value and does not contradict any annotation.

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 front-loaded with a purpose statement and trigger phrases, then logically moves through usage, return values, and critical caveats. Every sentence conveys important information, especially the could_not_verify warning and the 'replaces 4–6 calls' efficiency note. The opening list of phrases is slightly repetitive, but overall the structure earns its length 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?

With no output schema, the description carries the burden of explaining return values and behavior. It does so thoroughly: enumerates all verdicts, describes the actual value with pipeworx:// citation, explains could_not_verify and unsupported, and outlines the two processing paths. This provides enough context for an agent to use the tool correctly and interpret results, even 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?

Schema coverage is 100% and the schema already thoroughly explains both parameters, including tolerance_pct's override behavior and hallucination-detection usage. The description adds only a minor tie-in via 'exact percent-delta math,' which reinforces but does not extend the parameter meaning. This meets the baseline for well-documented schemas.

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 natural-language trigger phrases and explicitly states the core function: 'natural-language claim verification against authoritative sources.' It distinguishes the tool by covering both SEC EDGAR/XBRL financial claims and a grounded pipeline for other claims, and clearly names it as a replacement for 4–6 sequential calls. This is a specific verb+resource that differentiates it from sibling tools like ask_pipeworx or deep_research.

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-to-use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates the financial vs. non-financial paths. It does not explicitly state when not to use it or name alternative tools, but the 'Replaces 4–6 sequential calls' line provides implicit guidance against doing the multi-step pipeline manually. This is clear context but lacks exclusionary directing.

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