Kokoro 82M — fast, natural-sounding text-to-speech in multiple voices
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text input (long text is automatically split) | |
| speed | No | Speech speed multiplier (0.5 = half speed, 2.0 = double speed) | |
| voice | No | Voice to use for synthesis | af_bella |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It only states 'fast, natural-sounding', which are subjective quality claims, and fails to disclose output format, limitations, side effects, or any unobservable behavior. An AI agent would be unaware that this tool likely returns audio data.
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?
The description is a single, front-loaded sentence that is not bloated. However, the adjectives 'fast, natural-sounding' are subjective and do not contribute functional clarity, so it is concise but not maximally informative per word.
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?
With no output schema and only a minimal description, the tool is incomplete for an agent. It does not specify what the tool returns (e.g., audio file, URL), nor does it provide usage context beyond the generic 'text-to-speech' label. Given that sibling tools are all image/video, more operational detail is needed.
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?
The input schema fully documents all three parameters (text, speed, voice) with descriptions, defaults, and ranges, covering 100% of parameters. The tool description adds no additional parameter context, so the baseline score 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?
The description clearly identifies the tool as 'text-to-speech', which specifies the core function, and mentions 'multiple voices' to indicate scope. It distinguishes itself from the image/video generation siblings, but lacks an explicit action verb like 'synthesize' or 'generate', making it slightly less precise than ideal.
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 offers no explicit guidance on when to use this tool or when to avoid it. The sibling tools are all image/video generators, which implies this is the audio counterpart, but no direct comparison or exclusions are provided, leaving usage to be inferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Six of eight tools are for image generation, with overlapping capabilities and only subtle differentiators (speed, quality, text rendering). An agent cannot easily determine which model to use for a generic image generation request, leading to high ambiguity.
All tool names follow the same snake_case pattern using provider_model format (e.g., black_forest_labs_flux_pro, stability_ai_sdxl). The convention is uniform across the entire set, making it predictable and easy to parse.
The count of 8 tools is within the ideal range for a media studio, but the heavy redundancy of image generation models makes the set slightly bloated. Still, the overall scope is clear and manageable.
The tool surface covers the primary media generation modalities—image, video, and text-to-speech—which aligns with the 'Media Studio' purpose. Minor gaps exist (e.g., no image editing or audio-to-text), but core generation workflows are well covered.