Generate highly realistic Text to Speech voiceovers.
Scored across 3 tools
Each tool has a distinct, non-overlapping purpose: generate_tts creates audio, get_voices fetches voice options, and usage_statistic monitors quota. No ambiguity exists between them.
Tool names are readable and mostly follow a verb_noun pattern (generate_tts, get_voices), but 'usage_statistic' deviates by using a noun phrase without a verb. The style is consistent in snake_case, but the structural pattern is mixed.
Three tools is a well-scoped count for a TTS server, covering the essential operations: listing voices, generating speech, and checking API usage. No unnecessary tools or glaring omissions.
The core workflow (get voices -> generate TTS) is fully covered, with usage monitoring as a useful addition. Minor gaps might include advanced audio format specifications, but nothing that blocks typical usage.
Generates a Text to Speech audio voiceover using the provided voice object and written text string. This tool converts written text into a spoken audio file. It returns the direct download link, the duration of the audio, and the file size of the generated voiceover.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The written text string that you want to convert into a spoken audio voiceover. | |
| voice | Yes | The complete voice object retrieved from the get_voices tool. Do not modify or create this object manually; you must pass the exact object returned by the get_voices tool. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the output (download link, duration, file size) but does not mention any side effects, authentication requirements, rate limits, or costs. For a generation tool, these aspects could be relevant, but the core behavior is adequately described.
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, front-loaded with the action, and contains no filler. Every sentence adds value: the first explains the core function, the second lists the return values. It is concise and well-structured.
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?
The description covers the essential information: what the tool does, the inputs needed, and what it returns. It does not mention errors or edge cases, but for a straightforward TTS generation tool with a thorough schema, this is largely sufficient. The lack of an output schema is compensated by the explicit list of return values.
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 input schema already provides full descriptions for both parameters (100% coverage), including the voice object's required fields. The tool description itself adds no parameter-specific semantics beyond what the schema states, so the baseline of 3 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?
The description uses a specific verb ('Generates') and identifies the resource ('Text to Speech audio voiceover'), clearly stating it converts text to a spoken audio file. This distinguishes it from siblings like get_voices (retrieves voices) and usage_statistic (tracks usage) by focusing on audio 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 the tool is used when you have a voice object and text, but it does not explicitly state when to use it versus alternatives or mention prerequisites. The schema's note about using get_voices is not part of the tool description itself, so usage context is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves a comprehensive list of available Text to Speech voices used for generating audio voiceovers. You must call this tool first to obtain the required voice object before you can generate any Text to Speech audio. You can optionally filter the voices by providing a Language_code.
| Name | Required | Description | Default |
|---|---|---|---|
| language_code | No | Optional language code to filter the voices by (e.g. 'en-US', 'af-ZA'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the tool is a retrieval operation (non-destructive) and that it returns a list of voices, and emphasizes that obtaining a voice object is a prerequisite for TTS generation. It does not mention potential response size or error behavior, but for a simple list-retrieval tool this is adequate.
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?
Three sentences, each earning its place. The description is front-loaded with the tool's main purpose, followed by the critical prerequisite, then the optional parameter. No unnecessary words or repetition.
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 tool's simplicity (only one optional parameter, no output schema, no annotations), the description fully covers the essential information: what it does, when to use it, and how to use the parameter. It also aligns with sibling tool context by establishing the dependency on generate_tts.
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%; the schema already describes language_code as an optional filter. The description adds no new information about the parameter beyond what the schema provides, so the baseline of 3 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?
The description clearly states the tool retrieves a comprehensive list of Text to Speech voices, with a specific verb ('Retrieves') and resource. It also distinguishes itself from sibling tools by explicitly noting it must be called before generating any audio, which is the key differentiator.
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 provides explicit usage guidance: 'You must call this tool first to obtain the required voice object before you can generate any Text to Speech audio.' This clearly indicates when to use this tool versus generate_tts, and the optional filtering by language_code adds practical context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves the current user's API rate limits, including requests used and remaining requests. Use this to monitor your quota.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It transparently indicates a read-only operation ('Retrieves') and specifies the exact data returned (requests used and remaining), giving the agent confidence about its side-effect-free nature. It does not disclose auth requirements or error behavior, but for a simple retrieval this is adequate.
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 composed of two sentences, each adding value. The first states the core function and output, and the second provides a practical usage hint. It is front-loaded, concise, and free of 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?
Given the tool's simplicity (no parameters, no output schema), the description fully covers the essential information: what it does, what it returns, and when to use it. There are no missing details that would hinder an agent's ability to select and invoke the tool correctly.
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 has zero parameters, and the schema is empty with 100% coverage. Since there are no parameters to describe, the description doesn't need to add anything. The baseline for zero parameters is 4, and the description does not detract from it.
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 the tool's function using a specific verb ('Retrieves') and a specific resource ('the current user's API rate limits'), including the data it returns (requests used and remaining). This clearly distinguishes it from sibling tools like get_music and get_sfx, which deal with content retrieval.
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 offers explicit context for when to use the tool: 'Use this to monitor your quota.' While it does not mention when not to use it or explicitly compare with alternatives, the sibling tools are clearly in different domains, so the guidance is sufficient for a straightforward use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
generate_ttsget_voicesusage_statistic
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