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

The description adds substantial behavioral context beyond the annotations. Annotations only declare readOnly/openWorld/idempotent/non-destructive. The description discloses the return verdicts, the two routing paths, the meaning of could_not_verify (including verification_error details) versus unsupported, and the 'must not be shown as evidence' caveat. This is rich, decision-relevant transparency.

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 the core purpose and includes valuable examples, routing details, and error semantics. Every section earns its place for a tool with this complexity. It is not overly verbose or redundant, though a slightly tighter structure could improve scannability.

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 thoroughly explains return values (verdicts, actual value, citation, reasoning), error semantics (could_not_verify, unsupported), and usage scenarios. It also covers the two major claim classes. For a tool of this complexity, the description provides complete guidance for an 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?

Schema description coverage is 100%, so the schema fully documents both parameters including tolerance semantics. The description itself does not add further parameter-level detail beyond the schema (e.g., it mentions 'exact percent-delta math' but not specifically for tolerance). Baseline 3 is appropriate as the schema does the heavy lifting.

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 also includes trigger phrases and explicitly differentiates itself from generic fact-checking by describing two distinct pipelines (SEC EDGAR fast path vs grounded pipeline) and by noting it replaces 4-6 sequential calls. This distinguishes 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 provides an explicit 'when to use' statement: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also gives nuanced guidance on interpreting results (could_not_verify vs unsupported). However, it does not name alternative tools or state when NOT to use this tool in favor of a sibling, so it falls short of the highest bar.

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