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

Annotations already declare the tool as read-only, open-world, idempotent, and non-destructive. Beyond that, the description discloses critical behavioral details: the dual-path routing logic, the specific verdict types, the meaning of 'could_not_verify' with verification_error field, and a warning not to interpret it as evidence. It also notes the tool aggregates 4-6 calls, which is a useful execution insight for the agent.

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 information-dense, with every sentence contributing: example queries, path routing, verdict list, caller warning, and a benefit statement. It is front-loaded with the core purpose in the first sentence. While not as terse as a two-line description, the length is justified by the tool's complexity and the need to explain error semantics.

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 thoroughly: verdict types, the actual value with citation, reasoning, and the special meaning of could_not_verify vs unsupported. It covers the main scenarios (company-financial and generic claims), error handling, and the replacement-of-multiple-calls benefit. This is complete for an agent to invoke and interpret results correctly.

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?

Input schema covers both parameters with clear descriptions (100% coverage), so baseline is 3. The description adds extra meaning by explaining how tolerance_pct overrides the implied tolerance and how to use it for hallucination detection (set 1-2). It also provides examples for the claim parameter, reinforcing the schema. This goes beyond the schema's literal field descriptions.

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 function: natural-language claim verification against authoritative sources. It provides a specific verb ('validate', 'verify'), the resource (claims), and distinguishes itself from siblings by framing it as a consolidated verification tool that replaces multiple sequential calls. The examples ('Is it true that…', 'fact check') make the purpose instantly recognizable.

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,' which is strong when-to-use guidance. It also differentiates between company-financial claims (SEC EDGAR fast path) and other claims (grounded pipeline), giving routing context. However, it doesn't mention when not to use it or provide alternative tools for general fact lookup, though the routing logic implies alternatives exist.

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