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

Annotations already cover readOnly, idempotent, and closed-world behavior, so the description appropriately adds return-content context: verification status, source trust score, and evidence links. It still does not describe empty-result or pagination behavior, but it adds meaningful value beyond the annotations.

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?

Two tightly written sentences with the core purpose front-loaded and no wasted wording. The optional filtering detail follows naturally without burying the primary action.

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?

For a simple two-parameter read tool with full schema coverage and strong annotations, the description supplies enough return-value context to use it correctly. Nothing essential for selecting or invoking the tool appears missing.

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 both parameters are documented, giving a baseline of 3. The description adds concrete topic examples ("claude", "codex", "open weights") that clarify filtering semantics beyond the schema wording.

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?

States a specific verb ("Return") and resource ("today's checked AI claims") with a clear scope and examples of claim types. The "today's" scope and claim focus distinguish it from list_sources and broader search_claims usage without needing to name them.

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 clear context for when this tool is appropriate: retrieving today's checked AI claims, with optional topic filtering examples. It does not explicitly route historical or broad searches to search_claims, so there is a minor gap in alternative guidance.

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