check_tts_serviceCheck if the Brainiall Voice service is healthy and ready.
Returns: dict with keys: - status (str): 'healthy' or error state - modelLoaded (bool): Whether the synthesis model is loaded - version (str): API version
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds meaningful behavioral context beyond those hints by detailing the return keys: status, modelLoaded, and version, including the meaning of modelLoaded. This clarifies what 'healthy and ready' actually exposes to the caller.
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 brief, front-loaded with the core purpose, and then provides a clean structured list of return fields. There is no filler, and every sentence contributes usable information for the agent.
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 parameters and no output schema, the description carries the full burden of explaining the response. It documents all expected keys, their types, and the meaning of modelLoaded, making the tool fully callable and interpretable without additional lookup.
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 tool accepts zero parameters and schema coverage is 100%, so there is no parameter information for the description to add. The baseline for a no-parameter tool is 4, and the description appropriately focuses on return values instead of parameters.
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 states a clear verb+resource: 'Check if the Brainiall Voice service is healthy and ready.' The reference to 'synthesis model' ties it specifically to TTS, and the tool name reinforces that. It does not explicitly distinguish itself from sibling health-check tools like check_stt_service, but the TTS-specific wording is sufficient for basic differentiation.
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 gives no guidance on when to use this tool versus sibling health checks, nor does it mention any preconditions or alternatives. Usage context is only implied by the service name and the 'healthy and ready' phrasing, so an agent gets no explicit routing help.
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.
Most tools map to clearly distinct actions, but transcribe_audio and transcribe_audio_pro are near-duplicates and the four check_*_service tools require careful reading. The detailed descriptions make the distinctions recoverable, so confusion should be rare.
The set overwhelmingly follows a snake_case verb_* pattern, and the voice_id_* family is internally consistent. Minor deviations like get_phoneme_inventory vs list_tts_voices and the _pro suffix on transcribe_audio_pro keep it from being perfect.
Fourteen tools is within a reasonable range, but the server bundles pronunciation, STT, TTS, voice ID, and four health checks, making it feel broader than the 'Pronunciation' name suggests. A few health checks could be consolidated, but nothing is excessive.
Core pronunciation, transcription, and synthesis workflows are present, but the voice ID portion has create/list/verify/identify with no way to delete or unenroll a speaker. That is a notable lifecycle gap in an otherwise fairly complete speech toolkit.