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kie-mcp

by elibarnett
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JavaScript
Hybrid

generate_tts

Turn written text into spoken audio using ElevenLabs voices. Choose fast or high-quality models, set speed and language, and save the speech file directly to your system.

Instructions

Generate speech from text using ElevenLabs via kie.ai. Supports Turbo 2.5 (fast) and Multilingual V2 (high quality). Downloads to kie/assets/raw/.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to synthesize into speech
waitNoSet false to submit and return immediately with the task_id (async mode) — then poll with check_task and fetch with download_result. Recommended for long generations to avoid client-side watchdog timeouts.
modelNoturbo-2-5=fast, multilingual-v2=high quality with language supportturbo-2-5
speedNoSpeech speed (0.7–1.2). Only for multilingual-v2.
filenameNoOutput filename. Auto-generated if omitted.
voice_idNoVoice name (e.g. "Bella", "Viking Bjorn", "Aria") or kie voice ID. kie.ai only accepts its curated ~67-voice set — arbitrary ElevenLabs voice IDs are rejected. An unknown value returns the full catalog. Optional — defaults to James.
download_dirNoAbsolute directory to save the file(s) into (created if missing). Defaults to the server's kie/assets/raw/. Must be absolute — the MCP server's working directory is not the caller's.
language_codeNoLanguage code for multilingual-v2 (e.g. "en", "es", "fr", "ja")
max_wait_secondsNoOverride the blocking-mode polling budget in seconds (defaults: image 600, video 900, audio 300, speech 300). Ignored when wait=false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.7.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses model variants, async behavior, download location constraints, and voice_id limitations (curated set, rejection of arbitrary IDs). However, it does not explicitly state side effects (file creation) or auth requirements.

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 sentences that pack essential information with no redundancy. First sentence establishes core purpose and key differentiators; front-loaded for quick comprehension.

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 9 parameters and no output schema, the description covers models, async mode, and file location well. Missing details on return value (e.g., file path vs task ID) and output file format (e.g., .mp3). Adequate but not exhaustive.

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

Parameters5/5

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

With 100% schema coverage, the description adds significant value beyond the schema: explains wait rationale, voice_id caveats, download_dir absolute path requirement, and model selection advice. Each parameter gets meaningful context.

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 verb 'Generate speech from text', identifies the technology stack (ElevenLabs via kie.ai), and specifies output destination. This distinguishes it well from sibling tools like generate_music and generate_sfx.

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?

The description provides some usage context (model selection, async mode via wait parameter) but lacks explicit guidance on when to prefer this tool over alternatives like generate_gemini_tts. No comparative analysis with sibling tools.

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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