verify_claim

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

No annotations are provided, so the description carries full burden. It mentions returning verification with evidence but does not disclose behavior for invalid claims, rate limits, authentication requirements, or whether it is read-only. Key behavioral traits are missing.

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 sentences, front-loaded with the core purpose, and includes a concrete example. No wasted words; every sentence contributes meaning.

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?

Given the complexity of natural language parsing and evidence retrieval, the description is somewhat complete but does not explain the return format (e.g., confidence scores, evidence structure). No output schema is provided, so the description should cover this gap, but it falls short.

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% with accurate descriptions. The description adds value by explaining that the tool parses natural language claims and returns verification with evidence, which goes beyond the schema's parameter 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 verb 'verify' and the resource 'censorship claim', with an example that distinguishes it from sibling tools like agent_verify_message or atlas_fact_check by emphasizing natural language parsing and evidence linking.

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?

Provides examples of natural language claims but does not explicitly state when to use this tool versus alternatives (e.g., when evidence links are needed vs. simple verification). Usage is implied but not explicitly guided.

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