A
licenseA
qualityD
maintenanceProvides high-quality text-to-speech synthesis with 10 natural voices, emotion control, and dynamic pacing for professional applications requiring expressive speech output.
Last updated
5
2
MIT
tts
Server Details
Hosted pay-per-use TTS: 54 neural voices, 9 languages incl. Brazilian Portuguese. $10 free credits.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- fasuizu-br/brainiall-tts-mcp
- GitHub Stars
- 0
- Server Listing
- brainiall-tts-mcp
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Tool Definition Quality
A4.4/5.0
Tool DescriptionsA
Average 4.4/5 across 3 of 3 tools scored.
Server CoherenceA
Disambiguation5/5
Each tool has a clearly distinct purpose: health check, listing available voices, and converting text to speech. No overlap or ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (check_tts_service, list_voices, synthesize_speech).
Tool Count4/5
3 tools is minimal but appropriate for a TTS service, covering health monitoring, voice discovery, and speech synthesis. A slight expansion could include voice details, but current set is reasonable.
Completeness4/5
The tool surface covers the core operations: check service health, list voices, and synthesize speech. Minor gaps, such as the inability to retrieve a single voice's details without listing all, but overall adequate for the stated purpose.
Available Tools
3 toolscheck_tts_serviceCheck TTS ServiceARead-onlyIdempotentInspect
Check health status of the TTS API service.
Returns: dict with keys: - status (str): 'healthy' or error state
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint=false. The description adds disclosure of the return format (dict with status key), which is beyond annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two lines for the return description, no extraneous text. Every sentence is meaningful and focused.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no input schema and no output schema, but rich annotations, the description adequately describes the return format. However, it lacks detail on potential error states or interpretation beyond 'healthy' or error.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Zero parameters, so the input schema covers everything. The description adds no extra parameter info, but none is needed. Baseline 4 for no-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Check health status of the TTS API service', with specific verb ('check') and resource ('health status of TTS API service'). It distinguishes from sibling tools (list_voices, synthesize_speech) which do different tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when or when not to use this tool versus alternatives. Usage is implied for health checking, but no exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_voicesList VoicesARead-onlyIdempotentInspect
List available TTS voices with metadata.
54 neural voices across 9 languages. Each voice has an ID (used in synthesize_speech), display name, gender, accent and quality grade.
Args: language: Optional language filter (e.g. 'pt-br', 'en-us').
Returns: dict with keys: - voices (list): Each with id, name, gender, accent, lang, grade - count (int): Number of voices returned
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Filter by language code (e.g. 'pt-br', 'en-us', 'ja'); omit for all 54 voices |
Tool Definition Quality
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds value by detailing the number of voices (54), languages (9), and return structure (id, name, gender, accent, lang, grade). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Very concise: one-line purpose, bulleted args and returns. No fluff; every sentence adds value. Front-loaded with core verb and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity (1 optional param, no output schema), the description fully covers purpose, parameter usage, return format, and relation to siblings. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (1 parameter with description). The description adds real-world context: 'e.g. 'pt-br', 'en-us'' and default behavior ('omit for all 54 voices'), enriching the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List available TTS voices with metadata' and provides specifics (54 voices, 9 languages). It distinguishes itself from siblings: 'check_tts_service' (presumably service health) and 'synthesize_speech' (which uses a voice ID).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions optional language filter and notes the voice ID is used in 'synthesize_speech', guiding usage. However, it doesn't explicitly state when not to use this tool versus siblings, though the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
synthesize_speechSynthesize SpeechARead-onlyIdempotentInspect
Convert text to natural-sounding speech.
Returns WAV audio (16-bit PCM, 24 kHz mono) synthesized with neural voices. 54 voices across 9 languages -- including 3 native Brazilian Portuguese voices (pf_dora, pm_alex, pm_santa). Usage is metered per character.
Args: text: Text to convert to speech (max 5000 characters per request). language: Language code; selects the default voice when no voice is given. voice: Explicit voice ID (see list_voices). Overrides language. speed: Speech speed multiplier, 0.5-2.0. output_format: 'audio' for playable MCP audio content, 'base64_json' for a JSON object with the base64-encoded WAV and metadata.
Returns: MCP audio content (audio/wav), or when output_format='base64_json' a dict with keys: - audio_base64 (str): Base64-encoded WAV bytes - mime_type (str): 'audio/wav' - voice (str): Voice used - characters (int): Characters billed - estimated_cost_usd (float): Estimated cost of this request
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to convert to speech (1-5000 characters per request) | |
| speed | No | Speech speed multiplier (0.5 = half speed, 2.0 = double speed) | |
| voice | No | Voice ID (e.g. 'pf_dora', 'pm_alex', 'af_heart', 'bm_george'). Overrides 'language'. Use list_voices for the full catalog. | |
| language | No | Language code: 'pt' (Brazilian Portuguese, default), 'en', 'en-gb', 'es', 'fr', 'it', 'hi', 'ja', 'zh'. Used to pick a default voice when 'voice' is omitted. | pt |
| output_format | No | 'audio' (default) returns playable MCP audio content; 'base64_json' returns JSON with the base64-encoded WAV plus metadata | audio |
Tool Definition Quality
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. Description adds useful context: returns WAV format, neural voices, metered per character, character limit. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Very concise: intro sentence, two lines of key details, then well-organized Args and Returns sections. No superfluous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all aspects: input constraints (character limit), output formats with full return structure for base64_json, examples of voices, and cost indication. No missing information for a typical TTS tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so description adds value by providing examples (voice IDs, language codes) and clarifying output_format options. Does more than just repeat schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool converts text to speech, specifies the output format (WAV, 16-bit PCM, 24 kHz mono), and distinguishes from siblings like list_voices (lists voices) and check_tts_service (checks service).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides guidance on character limit, metered usage, and voice/language selection, but does not explicitly contrast with sibling tools or state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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