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recognize_audio_url

Transcribe speech from an online audio URL into text. Improve accuracy by providing context and language hints.

Instructions

识别在线音频URL中的文本

参数:

  • audio_url: 音频URL链接

  • context: 上下文文本,用于提高识别准确率(可选)

  • language: 指定语言代码(如 'zh', 'en'),可选,默认自动检测

  • model: 语音识别模型(可选,默认使用qwen3-asr-flash)

返回:

  • 识别的文本内容

注意: 需要设置环境变量 DASHSCOPE_API_KEY

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
contextNo
languageNo
audio_urlYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.5.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavior disclosure burden. It discloses the DASHSCOPE_API_KEY environment variable requirement, the default model, the auto-detect behavior for language, and the return type. It stops short of mentioning external data transmission, rate limits, or failure behavior, so it isn't perfect.

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 concise structured list with a one-line purpose, parameter definitions, return value, and a single operational note. No sentence is wasted, and the most important info is front-loaded.

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 the output schema and the description, the agent knows all parameters, defaults, the required API key, and what is returned. It lacks explicit usage-contrast guidance and format constraints for the URL, but those are minor for a straightforward transcription tool.

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?

Schema description coverage is 0%, and the description compensates by explaining every parameter: audio_url is the link, context is optional textual context for improved accuracy, language accepts codes like 'zh'/'en', and model defaults to qwen3-asr-flash. The defaults and optionality are explicitly stated.

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 states the verb 'recognize' and resource 'online audio URL' and result 'text' in the first line, making the tool's purpose unambiguous. It is naturally distinguished from sibling recognize_audio_file because it targets URLs rather than files.

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 implies the tool is for online audio URLs through its name and opening phrase, but does not explicitly contrast it with the sibling recognize_audio_file or other alternatives. There is no when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.