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

The description discloses valuable behavioral details beyond the annotations: async execution, a generation_id return, polling via get_generation, and pricing with a minimum charge. It does not cover auth, rate limits, or failure states, but the core async behavior is well communicated.

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 tight sentences: the first front-loads the core purpose and price, the second explains the async workflow. No filler or redundant restating of the schema, and every sentence carries actionable information.

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 two-parameter async tool with no output schema, the description adequately explains the return handle and polling pattern. It could add details about what happens when target_language is omitted and mention other possible statuses, but the essentials for correct invocation are present.

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 description coverage is 100%, so the schema already documents 'video' and 'target_language'. The description adds cost and polling context, but no extra parameter-level semantics such as accepted formats, defaults, or language selection behavior beyond the enum.

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 opens with 'Dub speech into 18 languages,' which specifies a concrete verb, object, and scope. This cleanly separates the tool from siblings like ai_speech_to_text, ai_subtitle_generator, and ai_video_to_audio, since dubbing to a target language is a distinct operation.

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 provides a clear workflow: run asynchronously, receive a generation_id, and poll get_generation until status is 'completed'. However, it does not explicitly state when to prefer this tool over alternatives or when not to use it, leaving sibling differentiation to inference.

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