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douyin-mcp-server

douyin-mcp-server

by maplezhang123
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

recognize_audio_file

Transcribe local audio files to text using SiliconFlow cloud ASR by default or a local Whisper model, enabling transcript extraction from recordings.

Instructions

识别本地音频；默认 SiliconFlow，也可选择本地 Whisper。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "file_path": "<file_path>"
    },
    "name": "recognize_audio_file"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNosmall
asr_modeNocloud
file_pathYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It hints at two backends (cloud SiliconFlow vs local Whisper) but never states that cloud mode requires network/API credentials, what audio formats or size limits apply, or how failures are surfaced – all material for an ASR tool.

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?

A single front-loaded sentence with no wasted words. It is efficiently structured, though the brevity is partly under-specification rather than disciplined concision.

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?

An output schema exists, so return values need not be explained. However, with zero annotations and 0% parameter coverage, the description should compensate by explaining the mode/model choices and any input constraints, and it does not.

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

Parameters2/5

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

Schema description coverage is 0% and none of the three parameters are documented in the schema. The description loosely maps to 'asr_mode' (cloud vs local) but never names the parameter or enumerates valid values, and the 'model' parameter (default 'small') is entirely unaddressed.

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 names a specific verb and resource ('识别本地音频' – recognize local audio), which is unambiguous and clearly distinct from the Douyin video siblings. It stops short of explicitly differentiating itself from those siblings, but the resource makes the boundary obvious.

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 mention of 'default SiliconFlow, also can choose local Whisper' describes backend selection, not when to use this tool versus alternatives or prerequisites. There is no guidance on when to prefer local vs cloud mode, or what inputs are acceptable.

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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