Transforme un texte en audio avec une voix bambara. Renvoie l'audio. Utiliser kooma_list_voices pour obtenir un identifiant de voix valide.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Le texte a lire a voix haute. | |
| voice | Yes | Identifiant de voix, obtenu via kooma_list_voices. | |
| format | No | Format de sortie. wav est sans perte, mp3 est plus leger. | wav |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool converts text to audio and returns it, which is useful, but it does not mention length limits, latency, output format handling, or any failure modes. Adequate but shallow.
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-loaded with the action and output, followed by the prerequisite. Every sentence earns its place and there is no filler.
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 simple text-to-speech tool, the description provides the essential purpose, output, and prerequisite. The schema covers the parameters, so nothing is missing for a correct call, though behavioral caveats and explicit sibling routing could make it more complete.
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%, so the baseline is 3. The description reinforces the voice parameter by specifying a Bambara voice and pointing to kooma_list_voices, but it adds little beyond what the schema already documents for text, voice, and format.
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 a specific action (transform text into audio), the resource (Bambara voice), and the output (returns the audio). This distinguishes it from siblings like kooma_transcribe_from_url and kooma_translate, while also pointing to kooma_list_voices as the source of valid voices.
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?
It gives a clear prerequisite: use kooma_list_voices to obtain a valid voice identifier. It does not explicitly state when not to use the tool versus transcription/translation, but the purpose is clear enough that an agent can infer the right context.
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.
Each tool targets a clearly distinct operation: voice discovery, speech synthesis, transcription from a URL, and translation. The only related pair, kooma_list_voices and kooma_text_to_speech, is explicitly designed as a dependency rather than an overlap.
All tools share the consistent kooma_ prefix and snake_case convention, with most names using straightforward verbs. kooma_text_to_speech breaks the verb-led pattern slightly, but the overall naming scheme remains predictable and readable.
With four tools, the server is tightly scoped to its core Bambara language tasks: text-to-speech, transcription, and translation. Every tool serves a distinct function and none feels redundant or extraneous.
The set covers the essential operations for each advertised capability, including a voice-list helper before synthesis and transcription from public URLs. Minor gaps such as a language-list helper for translation or local-file transcription are workable but not critical.
