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

The description discloses rich behavioral details beyond the annotations: it distinguishes between structured financial verification and grounded fallback, enumerates the verdict types, and explicitly warns that 'could_not_verify' means the check did not happen and must not be treated as evidence. It also explains 'unsupported' semantics and the exact source of citations, going far beyond the readOnly/openWorld/idempotent 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 is front-loaded with user intent phrases and a clear purpose. Each sentence earns its place: purpose, routing, overrides, caller warning, and performance benefit. It is well-structured and not wasteful, though it could be slightly trimmed without losing critical 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?

Despite having no output schema, the description compensates fully by listing the exact verdicts that will be returned, the presence of a 'pipeworx://' citation and reasoning, and the error/edge-case behaviors (could_not_verify, unsupported). It also explains the routing for different claim types, making the tool's behavior predictable and complete for the agent.

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 already provides 100% coverage for both claim and tolerance_pct, including the default behavior and suggested values for hallucination detection. The description's mention of 'exact percent-delta math' loosely relates to tolerance_pct but adds little meaning beyond what the schema already states, so the baseline of 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 defines a specific verb+resource+scope: 'natural-language claim verification against authoritative sources' with concrete user phrasings like 'fact check' and 'verify the claim that…'. It clearly distinguishes itself from siblings such as ask_pipeworx or deep_research by its claim-verification focus and routing logic (SEC EDGAR/XBRL vs grounded pipeline).

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' giving a clear when-to-use condition. It also explains that it replaces 4–6 sequential calls, implying it is an aggregated tool, though it does not explicitly name alternatives to avoid.

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