Audio Transcriber MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_MODEL | No | Optional OpenAI model to use for transcription | |
| OPENAI_API_KEY | Yes | Your OpenAI API key | |
| OPENAI_BASE_URL | No | Optional custom OpenAI API base URL |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| transcribe_audioC | Transcribe an audio file using OpenAI Whisper API |
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 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and singular, making it impossible for an agent to misselect between non-existent alternatives.
The single tool name follows a clear verb_noun pattern (transcribe_audio), and with only one tool, there is no inconsistency to evaluate. The naming is straightforward and aligns with common conventions.
A single tool is too few for a server named 'Audio Transcriber MCP Server', as it suggests a limited scope that may not cover related operations like transcription status checks, file management, or configuration. This minimal set could hinder agent workflows that require more comprehensive functionality.
The tool set is severely incomplete for audio transcription tasks. While it provides a core transcription function, it lacks essential operations such as listing available transcriptions, retrieving transcription results, handling errors, or managing audio files, which are typical in such domains and could lead to agent failures.