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tusharpatil2912

Pollinations Multimodal MCP Server

listAudioVoices

Retrieve available audio voices for text-to-speech generation in the Pollinations Multimodal MCP Server.

Instructions

List available audio voices

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function that implements the listAudioVoices tool. It fetches the list of available audio voices from the Pollinations API (models endpoint), extracts voices from the openai-audio model, falls back to defaults if needed, and returns them in MCP response format.
    async function listAudioVoices(params) {
        try {
            const url = buildUrl(AUDIO_API_BASE_URL, "models");
            const response = await fetch(url);
    
            if (!response.ok) {
                throw new Error(`Failed to list models: ${response.statusText}`);
            }
    
            const models = await response.json();
    
            // Find the openai-audio model and extract its voices
            const audioModel = models.find(
                (model) => model.name === "openai-audio",
            );
    
            let voices;
            if (audioModel && Array.isArray(audioModel.voices)) {
                voices = audioModel.voices;
            } else {
                // Default voices if we can't find the list
                voices = ["alloy", "echo", "fable", "onyx", "nova", "shimmer"];
            }
    
            // Return the response in MCP format using utility functions
            return createMCPResponse([createTextContent(voices, true)]);
        } catch (error) {
            console.error("Error listing audio voices:", error);
            // Return default voices if there's an error
            const defaultVoices = [
                "alloy",
                "echo",
                "fable",
                "onyx",
                "nova",
                "shimmer",
            ];
    
            // Return the response in MCP format using utility functions
            return createMCPResponse([createTextContent(defaultVoices, true)]);
        }
    }
  • The registration entry for the listAudioVoices tool in the audioTools array exported from audioService.js. This array is later spread into the main toolDefinitions in index.js and registered with the MCP server.
    ["listAudioVoices", "List available audio voices", {}, listAudioVoices],
  • src/index.js:30-30 (registration)
    Spreading of audioTools (which includes listAudioVoices registration) into the main toolDefinitions array used for MCP server tool registration.
    ...audioTools,
  • src/index.js:87-87 (registration)
    Dynamic registration of all tools, including listAudioVoices, by iterating over toolDefinitions and calling server.tool(...tool) for each.
    toolDefinitions.forEach((tool) => server.tool(...tool));
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states the basic action. It doesn't disclose behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, pagination, or the format of returned data, which are critical for a tool with zero parameters.

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?

The description is a single, efficient sentence with no wasted words, front-loading the core purpose ('List available audio voices'). It's appropriately sized for a simple tool with no parameters.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema), the description is minimal but incomplete. It lacks context on authentication needs, return format, or how the output integrates with sibling tools like 'respondAudio', making it insufficient for full agent understanding without external cues.

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?

The tool has zero parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add parameter details, but this is appropriate given the lack of parameters, earning a baseline score above minimum viable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('available audio voices'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'listImageModels' or 'listTextModels' beyond the resource type, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., authentication), related tools like 'respondAudio' or 'sayText', or any context for selecting voices, leaving the agent with minimal usage direction.

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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