whisper-transcribe-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| WHISPER_MODEL | No | Local model size: tiny, base, small, medium, large-v3 | base |
| OPENAI_API_KEY | No | If set, activates the OpenAI backend instead of local |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| transcribe_fileC | Transcribe an audio file to text. |
| transcribe_base64C | Transcribe audio provided as a base64-encoded string. |
| list_modelsA | List available Whisper model sizes and current configuration. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: transcribe_file (local file input), transcribe_base64 (encoded string input), and list_models (metadata/discovery). The file-vs-base64 split is unambiguous because it is keyed to input source, not overlapping functionality.
All three tools follow a consistent snake_case verb_noun pattern (transcribe_file, transcribe_base64, list_models). The convention is predictable and readable throughout.
Three tools is well-scoped for a narrow, single-purpose transcription server; each tool covers a genuinely distinct need (file input, string input, model discovery) with no redundancy.
The core transcribe-and-inspect lifecycle is covered, but common Whisper capabilities like translation, language detection, or alternate output formats (SRT/VTT) are absent. These are minor gaps an agent can work around given the stated purpose.