text_to_dialogue

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

With no annotations, the description carries the full burden and does real work: it discloses that long scripts are chunked into ~2000-character requests with whole lines preserved, that chunks are joined, and that local execution requires ffmpeg. Audio tags steering delivery is valuable behavioral context. It omits cost/latency, auth requirements, and what happens on partial failure.

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 dense sentences with the core identity front-loaded and operational caveats (chunking, ffmpeg) trailing. Every clause carries information, though the parenthetical about chunk sizes makes the first sentence heavy.

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 7-parameter, no-annotation, no-output-schema tool, the description covers the essentials: input shape, model, chunking behavior, delivery tags, and the tier-gated output formats. Remaining gaps (return value, seed behavior, error handling) are modest given no output schema exists to document returns.

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 57%, so several parameters (seed, model_id, lines) rely on the description. It explains the lines structure (text + voice_id per line), names the default model eleven_v3, and documents audio tags embedded in text — genuine additions. However, seed and the interaction between stability/language_code and dialogue output go unaddressed.

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 capability — multi-speaker conversation from a script of ordered lines — plus the model and a concrete use case ('core podcast tool'). The 'multi-speaker' framing implicitly separates it from the single-voice text_to_speech sibling, so an agent can route without opening schemas.

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?

Usage context is implied rather than stated: 'the core podcast tool' and 'multi-speaker' suggest when to pick this over text_to_speech, but it never names the alternative or the when-not condition. The instruction to 'pass the script as ordered lines' is invocation guidance, not selection guidance.

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