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extract_douyin_text

Extract spoken text from Douyin videos by providing a share link. Uses local speech recognition, and with extra context, organizes raw transcript into polished spoken copy.

Instructions

从抖音分享链接提取视频中的文本内容

参数:

  • share_link: 抖音分享链接或包含链接的文本

  • model: 本地 faster-whisper 模型(可选,默认 medium)

  • context: 口播整理用途或额外要求(可选)

返回:

  • 提取的文本内容

注意: 本地 ASR 不消耗 API;最后整理口播需要 DOUYIN_DEEPSEEK_API_KEY

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
contextNo
share_linkYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.5.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It usefully discloses that local ASR does not consume API quota and that final organization requires a DeepSeek API key. It does not mention failure modes, rate limits, video length constraints, or whether the tool downloads the video, so transparency is partial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a one-sentence purpose, a parameter list, a return note, and an important API-key note. It is concise and front-loaded, with no significant redundancy, though the return line adds little beyond the output schema.

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

Completeness3/5

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

The description covers inputs, key dependencies, and a basic return value, which is enough for simple invocations. However, it lacks guidance on choosing between sibling tools, expected output format details, and operational constraints, so completeness is only moderate.

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?

Schema description coverage is 0%, but the description compensates by explaining all three parameters: share_link can contain the link or surrounding text, model defaults to medium and is optional, and context defines the organization purpose or extra requirements. It adds meaningful semantics beyond the bare schema, though model choices and context format remain underspecified.

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 states a clear action: extracting text content from a Douyin video via a share link. It implies differentiation from siblings like get_douyin_download_link and recognize_audio_file/url by targeting share links and extracted text, though it does not explicitly name those alternatives.

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 usage when a Douyin share link is provided and text/口播 content is desired, and notes when DOUYIN_DEEPSEEK_API_KEY is required. However, it does not explicitly state when to prefer this tool over siblings such as recognize_audio_file, recognize_audio_url, or parse_douyin_video_info.

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