generate_fake_chat

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

No annotations provided, so description carries full burden. It discloses that output is watermarked and clearly fictional, but does not detail other behavioral aspects like authentication, rate limits, or side effects. The parameter schema is rich, but description adds only the watermarking point.

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-load the core purpose and add necessary usage warnings without redundancy. Every part contributes 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 7 parameters and no output schema, the description explains that output is a deep link and watermarked. It covers the essential context for using the tool, though it could elaborate on the return structure for different formats.

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 descriptive property descriptions. The description does not significantly add meaning beyond the schema, except for reinforcing the platform list. Baseline of 3 is appropriate.

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?

Description clearly states the tool composes a fake chat screenshot for various platforms and provides a pre-filled deep link. It mentions specific use cases (parody, education, design mockups, fiction) and distinguishes from siblings by focusing on generation rather than listing devices or platforms.

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?

Description explicitly states the output is watermarked and intended for legitimate purposes, not deception. While it gives usage context, it does not directly contrast with sibling tools (list_devices, list_platforms) that could be used to explore available options.

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