verify_text_origin

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

With no annotations, the description thoroughly covers side effects (read-only), authentication (none for basic, API key for Pro), rate limits (10/day free), error handling (structured errors), idempotency, and data privacy. Exceeds what annotations would provide.

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?

Well-structured with clear headings (Args, Returns, Behavior, When to use, etc.) and front-loaded purpose. Slightly verbose but all content adds value.

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 2 params, no output schema, no annotations, the description fully explains inputs (with constraints), outputs (confidence, classification, breakdown), behavior, and limitations. Complete for agent decision-making.

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 0%, so description must compensate. It adds min 50 chars for 'text' and explains 'api_key' usage in the behavioral section (though not in the param list). Adds significant meaning beyond 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 analyzes text for AI-generated patterns using specific techniques (perplexity, burstiness, repetition, phrases), and distinguishes from siblings like detect_deepfake_image which focuses on images.

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 explicit 'When to use' and 'When NOT to use' sections, including a caution about real-time decisions without human review. Lacks direct comparison to sibling tools but covers context well.

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