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
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Tool Definition Quality
Average 4.3/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: health check, voice listing, and speech synthesis. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern (check_tts_service, list_voices, synthesize_speech) with snake_case, making them predictable and easy to understand.
The server has exactly 3 tools, which is well-scoped for a TTS service. Each tool covers a core function: health check, voice discovery, and speech generation, with no unnecessary additions.
The tool surface covers all essential TTS operations: health monitoring, voice listing (with filtering), and text-to-speech synthesis with multiple options. There are no obvious gaps 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
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds value by specifying the return format (dict with status key), which goes beyond the 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no extraneous information. It is front-loaded with the action and follow with return 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?
For a zero-parameter, simple health check tool, the description fully covers the purpose and return structure. No gaps given the simplicity and existing annotations.
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?
With 0 parameters, baseline is 4. The description does not need to add parameter details, and it correctly focuses on the return value instead.
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 uses a specific verb ('Check') and resource ('TTS service'), clearly stating the action and target. It distinguishes from siblings (list_voices, synthesize_speech) by focusing on health status rather than voice listing or speech generation.
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 implies usage as a readiness check before other TTS operations, but does not explicitly state when to use it versus alternatives or provide exclusions. The context and sibling tool names make the usage inferable, but no direct guidance is given.
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
| 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
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. Description adds return structure (dict with voices and count, each with id, name, gender, accent, lang, grade) and notes language filter behavior. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is very concise: two sentences plus bulleted lists for args and returns. No unnecessary words. Information is front-loaded.
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?
Given no output schema, description provides detailed return format. Also notes the voice ID usage in synthesize_speech. Covers all critical aspects for a list tool with one optional parameter.
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% for the single parameter 'language'. Description adds example codes and clarifies it's optional, which adds marginal value. Baseline score 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?
Description uses specific verb 'list' and resource 'available TTS voices with metadata', mentions 54 voices across 9 languages, and connects to sibling synthesize_speech by noting the voice ID is used there. Clearly distinguishes from check_tts_service.
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?
Describes what the tool does ('list available voices') and notes optional language filter. While not explicitly stating when not to use, the sibling context implies use before synthesize_speech. Could be more explicit about alternatives.
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
| 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
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond annotations: audio specifics (PCM, kHz, mono), voice catalog details, and metering. Annotations are present but the description enriches transparency with 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a short intro, then details, then Args. It is front-loaded with key info and every sentence adds value. No unnecessary content.
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?
Given the complexity (5 parameters, no output schema), the description fully explains the tool: input constraints, output formats, voice options, and metering. No gaps are apparent.
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 description coverage is 100%, so description adds minimal extra value for parameters. The Args section largely duplicates schema descriptions. No significant new parameter semantics provided.
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 'Convert text to natural-sounding speech' and specifies the output format (WAV 16-bit PCM, 24 kHz mono). It distinguishes from siblings by mentioning voice IDs and referencing list_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?
The description explains the tool returns audio and mentions metered usage, but does not explicitly state when to use it over alternatives like check_tts_service or list_voices. It provides enough context for common use cases.
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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