AutoGLM ASR MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@AutoGLM ASR MCP Servertranscribe /home/user/meeting_recording.wav"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AutoGLM ASR MCP Server
MCP server for high-quality speech-to-text transcription using Zhipu AutoGLM ASR.
CN: 一个面向 Agent 的语音转文字 MCP 服务,支持长音频分块、上下文传递和时间戳分段。
For AI-oriented setup details, see AI_SETUP_GUIDE.md.
For AI Agents (TL;DR)
Type: MCP Server
Domain: ASR / speech-to-text / transcription
Input: local audio file path
Output: full transcript text + timestamp segments
Best for: meeting notes, call analysis, subtitle draft, voice memo transcription
Supported audio formats:
mp3,wav,m4a,flac,ogg,webmCore tools:
transcribe_audio,get_audio_info
Related MCP server: Whisper Speech Recognition MCP Server
What It Does
Transcribes short and long audio files with automatic chunking.
Uses context-aware modes to balance speed and quality.
Returns readable full text and segment-level timestamps.
Runs over stdio as an MCP server for coding assistants.
Tool Index
Tool | Purpose | Required Args | Optional Args | Returns |
| Transcribe audio to text |
|
| Full transcript and time-aligned segments |
| Inspect audio before transcription |
| None | Duration, format, channels, sample rate, estimated chunks |
Features
Fast long-audio transcription with sliding-window concurrency.
Better accuracy through chunk-to-chunk context passing.
Automatic splitting for long inputs (API limit friendly).
Zero-install runtime with
npx.Works with common MCP clients.
Installation
Prerequisites
ffmpeg must be installed:
# macOS
brew install ffmpeg
# Ubuntu/Debian
apt install ffmpeg
# Windows
choco install ffmpegGet your API key from Zhipu AI Open Platform.
NPX (Recommended)
npx autoglm-asr-mcpQuick Start
Add this MCP server to your client config and set AUTOGLM_ASR_API_KEY.
{
"mcpServers": {
"autoglm-asr": {
"command": "npx",
"args": ["-y", "autoglm-asr-mcp"],
"env": {
"AUTOGLM_ASR_API_KEY": "your-api-key"
}
}
}
}Compatibility
Claude Desktop / Claude Code
Cursor
Windsurf
VS Code MCP
Other MCP-compatible clients
VS Code quick install:
Tools
transcribe_audio
Transcribe an audio file into text with timing segments.
Arguments:
Name | Type | Required | Description |
| string | Yes | Absolute path to the audio file |
| string | No |
|
| integer | No | Max parallel requests, range |
Returns:
Full transcription text
Timestamped segment list
Basic run stats (chunks, mode, elapsed time)
Common errors:
File not found or unreadable path
Unsupported format or broken audio stream
Missing/invalid API key
get_audio_info
Inspect an audio file before transcription.
Arguments:
Name | Type | Required | Description |
| string | Yes | Absolute path to the audio file |
Returns:
Duration
Format
Sample rate
Channels
Estimated chunks
Context Modes
Mode | Speed | Quality | Description |
| Fast | High | First chunk initializes context, later chunks run in parallel with context |
| Fastest | Medium | Chunks run independently in parallel |
| Slow | Best | All chunks transcribed sequentially with full context chain |
Environment Variables
Variable | Default | Description |
| required | Your Zhipu API key |
|
| API endpoint |
|
| ASR model name |
|
| Max chunk duration (seconds) |
|
| Default concurrency |
|
| Max context size passed between chunks |
Use Cases
Meeting recording to editable transcript
Customer support call transcription
Podcast/video subtitle draft generation
Voice memo indexing and search
Limitations
Requires local file path input (not remote URL input).
Audio quality strongly affects transcription quality.
Very noisy or multi-speaker overlap can reduce accuracy.
Troubleshooting
ffmpeg not found: install ffmpeg and retry.File not found: pass an absolute existing path.API errors: verify
AUTOGLM_ASR_API_KEYand account quota.
Keywords
mcp, model-context-protocol, asr, speech-to-text, transcription, autoglm, zhipu, chinese-asr, audio-transcription, meeting-transcript, subtitle-generation, voice-to-text, agent-tools, llm-tools, coding-agent
License
MIT
Available Tools
2 toolsget_audio_infoA
Get information about an audio file (duration, format).
Use this to check audio length before transcription.
| Name | Required | Description | Default |
|---|---|---|---|
| audio_path | Yes | Absolute path to the audio file |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. 'Get information' clearly implies a read-only operation, and it discloses the specific information retrieved (duration, format). No side effects or limitations are mentioned, but for a simple metadata retrieval tool, this is sufficiently transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the purpose, and no wasted words. Every sentence earns its place, and it reads naturally.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description fully covers what it does (returns duration and format), and the use case ('check audio length before transcription') provides valuable context. It is complete and actionable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (audio_path described as 'Absolute path to the audio file'). The description adds no extra meaning beyond the schema, but the schema is sufficient. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Get information about an audio file (duration, format).' This is a specific verb+resource combination, and it distinguishes itself from the sibling tool transcribe_audio by explicitly connecting the use case to transcription preparation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context with 'Use this to check audio length before transcription.' This implies a specific scenario and distinguishes it from the sibling, though it doesn't explicitly state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
transcribe_audioA
Transcribe an audio file to text using AutoGLM ASR.
Supports: mp3, wav, m4a, flac, ogg, webm
Features:
Automatic chunking for long audio (>30s)
Sliding window concurrency for speed + quality
Context passing between chunks for better accuracy
Args: audio_path: Absolute path to the audio file context_mode: "sliding" (recommended), "none" (fastest), or "full_serial" (best quality but slow) max_concurrency: Max parallel API requests (default: 5)
Returns: Full transcription text with timing segments
| Name | Required | Description | Default |
|---|---|---|---|
| audio_path | Yes | Absolute path to the audio file to transcribe | |
| context_mode | No | Context strategy: 'sliding' (balanced), 'none' (fastest), 'full_serial' (best quality) | sliding |
| max_concurrency | No | Maximum number of concurrent API requests |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It fully discloses key behaviors: automatic chunking for long audio, sliding window concurrency, context passing, and return of timing segments. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a one-sentence purpose, a support list, feature bullets, Args, and Returns. Every sentence adds value, and it is easy to scan. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a moderately complex tool with no output schema, the description covers supported formats, behavior for long audio, parameter modes, concurrency limits, and the return type. It could add more detail on the structure of timing segments or error handling, but it is complete enough for tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining trade-offs and recommendations for context_mode (e.g., 'sliding' recommended, 'full_serial' best quality but slow) and clarifying the purpose of max_concurrency. This enhances semantic understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Transcribe an audio file to text using AutoGLM ASR.' It clearly distinguishes from sibling tool get_audio_info by focusing on transcription rather than audio metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (any transcription need) and gives detailed parameter guidance (e.g., recommended context_mode). It doesn't explicitly mention alternatives or when not to use the tool, but the sibling tool is obviously different, so the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool has a clearly distinct purpose: transcribe_audio performs the core speech-to-text conversion, while get_audio_info retrieves file metadata for pre-checking. There is no overlap or ambiguity between them.
Both tools follow a consistent verb_noun snake_case convention (transcribe_audio, get_audio_info), making the API predictable and easy to navigate.
The server has only 2 tools, which feels slightly thin for a dedicated ASR service. However, the tools cover the essential workflow (transcription + metadata check), so the count is not inappropriate.
The core transcription workflow is fully covered, and get_audio_info enables users to verify audio properties before transcribing. Minor gaps such as listing supported formats or job management exist, but these are not critical for the server's stated purpose.
Maintenance
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