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

Even though annotations already mark this as read-only, idempotent, and non-destructive, the description adds substantial behavioral context: it enumerates all six possible verdicts, explains the crucial distinction between could_not_verify and unsupported, warns that could_not_verify must not be treated as evidence, and discloses the verification_error structure. This goes far beyond the annotations and is critical for correct agent behavior.

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 well-structured and front-loaded with the trigger phrases and purpose. It includes an 'IMPORTANT for callers' section and an efficiency statement, all of which are valuable. It's longer than minimal descriptions, but the tool's complexity justifies the length; no sentence is wasted.

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 full burden of explaining return values. It clearly specifies the verdict values, the actual value with pipeworx:// citation, and reasoning. It also covers edge cases like could_not_verify and unsupported, making the description complete for the tool's complexity and leaving no ambiguity about how to interpret results.

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 meaning by explaining how tolerance_pct interacts with grading (e.g., 'set 1–2 for hallucination detection') and how the claim parameter is used in examples like 'Apple's FY2024 revenue was $400 billion' (the latter also appears in the schema). This is a meaningful enhancement beyond the schema.

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 the tool's purpose: natural-language claim verification against authoritative sources. It provides specific trigger phrases and example claims, and distinguishes itself from sibling tools like ask_pipeworx by describing the dedicated financial and grounded pipelines. This is a specific verb+resource+scope description, not a tautology.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the routing logic for financial vs. other claims. It also notes it 'Replaces 4–6 sequential calls,' indicating when it's the consolidated choice. However, it does not name alternative tools or explicitly state when NOT to use it, 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.