Traduit un texte entre le bambara et 13 autres langues. Les codes de langue combinent langue et ecriture : bam_Latn (bambara), fra_Latn (francais), eng_Latn (anglais), et ainsi de suite.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Le texte a traduire. | |
| source | No | Code de la langue source. Facultatif : detecte sinon. | |
| target | Yes | Code de la langue cible, par exemple bam_Latn. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It adds useful scope context (supported languages and code format), but it does not disclose output format, source detection behavior, limits, or side effects. 'Traduit' implies a safe read-like transformation, so this is not a severe gap.
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 states the core purpose, the second gives the essential code-format guidance. No filler or redundant repetition of schema details.
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?
Adequate for a simple translation tool, but the '13 autres langues' set is not enumerated and no reference to accepted codes is provided, so an agent may not know all valid target codes. Return behavior is implicit but not critical.
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 adds value by explaining the combined language-script code scheme with concrete examples (bam_Latn, fra_Latn, eng_Latn), helping agents construct valid source and target values.
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 uses a specific verb ('Traduit un texte') and identifies the resource ('entre le bambara et 13 autres langues'), clearly distinguishing it from sibling tools like TTS, transcription, or voice listing.
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 by the translation verb and language set, but there is no explicit when-to-use statement or comparison with siblings such as kooma_text_to_speech. The optional source detection is mentioned in the schema, not the description.
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.
