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tusharpatil2912

Pollinations Multimodal MCP Server

sayText

Convert text into spoken audio using customizable voices and formats for accessible content creation.

Instructions

Generate speech that says the provided text verbatim

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to speak verbatim
voiceNoVoice to use for audio generation (default: "alloy")
formatNoFormat of the audio (mp3, wav, etc.)
voiceInstructionsNoAdditional instructions for voice character/style (e.g., "Speak with enthusiasm" or "Use a calm tone")

Implementation Reference

  • The handler function that executes the sayText tool: generates verbatim text-to-speech audio using the Pollinations API, supports voice, format, instructions, and optional playback.
    async function sayText(params) {
        const {
            text,
            voice = "alloy",
            format = "mp3",
            voiceInstructions,
            audioPlayer,
            tempDir,
        } = params;
    
        if (!text || typeof text !== "string") {
            throw new Error("Text is required and must be a string");
        }
    
        // Prepare the query parameters
        const queryParams = {
            model: "openai-audio",
            voice,
            format,
        };
    
        // Prepare the prompt with the verbatim instruction
        let finalPrompt = `Say verbatim: ${text}`;
    
        // Add voice instructions if provided
        if (voiceInstructions) {
            finalPrompt = `${voiceInstructions}\n\n${finalPrompt}`;
        }
    
        // Build the URL using the utility function
        const url = buildUrl(
            AUDIO_API_BASE_URL,
            encodeURIComponent(finalPrompt),
            queryParams,
        );
    
        try {
            // Fetch the audio from the URL
            const response = await fetch(url);
    
            if (!response.ok) {
                throw new Error(
                    `Failed to generate speech: ${response.statusText}`,
                );
            }
    
            // Get the audio data as an ArrayBuffer
            const audioBuffer = await response.arrayBuffer();
    
            // Convert the ArrayBuffer to a base64 string
            const base64Data = Buffer.from(audioBuffer).toString("base64");
    
            // Determine the mime type from the format
            const mimeType = `audio/${format === "mp3" ? "mpeg" : format}`;
    
            // Play the audio if an audio player is provided
            if (audioPlayer) {
                const tempDirPath = tempDir || os.tmpdir();
                await playAudio(
                    base64Data,
                    mimeType,
                    "say_text",
                    audioPlayer,
                    tempDirPath,
                );
            }
    
            // Return the response in MCP format
            return createMCPResponse([
                {
                    type: "audio",
                    data: base64Data,
                    mimeType,
                },
                createTextContent(
                    `Generated audio for text: "${text}"\n\nVoice: ${voice}\nFormat: ${format}`,
                ),
            ]);
        } catch (error) {
            console.error("Error generating audio:", error);
            throw error;
        }
    }
  • Input schema using Zod for validating parameters of the sayText tool: text (required), voice, format, voiceInstructions (optional).
    {
        text: z.string().describe("The text to speak verbatim"),
        voice: z
            .string()
            .optional()
            .describe(
                'Voice to use for audio generation (default: "alloy")',
            ),
        format: z
            .string()
            .optional()
            .describe("Format of the audio (mp3, wav, etc.)"),
        voiceInstructions: z
            .string()
            .optional()
            .describe(
                'Additional instructions for voice character/style (e.g., "Speak with enthusiasm" or "Use a calm tone")',
            ),
    },
    sayText,
  • Registration entry for the sayText tool in the audioTools export array, formatted for MCP server.tool() calls.
    [
        "sayText",
        "Generate speech that says the provided text verbatim",
        {
            text: z.string().describe("The text to speak verbatim"),
            voice: z
                .string()
                .optional()
                .describe(
                    'Voice to use for audio generation (default: "alloy")',
                ),
            format: z
                .string()
                .optional()
                .describe("Format of the audio (mp3, wav, etc.)"),
            voiceInstructions: z
                .string()
                .optional()
                .describe(
                    'Additional instructions for voice character/style (e.g., "Speak with enthusiasm" or "Use a calm tone")',
                ),
        },
        sayText,
    ],
Behavior2/5

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 states the tool generates speech, implying an output, but doesn't describe what that output is (e.g., audio file, stream), any side effects, rate limits, or authentication needs. For a tool with no annotations, this is a significant gap in transparency.

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: 'Generate speech that says the provided text verbatim.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for this tool's complexity. Every word earns its place.

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 moderate complexity (4 parameters, no output schema, no annotations), the description is incomplete. It lacks details on the output (e.g., audio format, how it's returned), behavioral traits like side effects or errors, and usage context. Without annotations or an output schema, the description should do more to compensate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all four parameters (text, voice, format, voiceInstructions). The description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints. This meets the baseline of 3, as the schema handles the heavy lifting.

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 tool's purpose: 'Generate speech that says the provided text verbatim.' It specifies the verb ('Generate speech') and resource ('the provided text'), making the function immediately understandable. However, it doesn't distinguish this from sibling tools like 'respondAudio' or 'listAudioVoices', 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'respondAudio' (which might handle conversational audio) or 'listAudioVoices' (which could list available voices), nor does it specify prerequisites or contexts for use. This leaves 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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