jev-av-analysis-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| YT_DLP_BIN | No | Binary path for yt-dlp | yt-dlp |
| WHISPER_BIN | No | Binary path for whisper | whisper-cli |
| JEV_MCP_MOCK | No | 1 forces Jev mock | 0 |
| WHISPER_MODEL | No | Model name hint | base.en |
| MEDIA_MCP_MOCK | No | 1 skips transcription binaries | 0 |
| TYPESAFE_API_KEY | No | Required for real Jev calls; blank falls back to mock | |
| WHISPER_MODEL_PATH | No | Absolute path to the .ggml model file | |
| MEDIA_MCP_TIMEOUT_MS | No | Transcription timeout | 600000 |
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
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| transcribe_mediaA | Turn any video/audio URL or local file into a transcript, locally and key-free. URLs are fetched with yt-dlp; local files and fetched audio are transcribed with whisper.cpp. Returns the full transcript text plus optional [start --> end] segments. |
| fetch_mediaA | Download the best audio from a video/audio URL to a local file using yt-dlp. Returns the local path plus title and duration. No API key required. |
| transcribe_fileC | Transcribe a local audio/video file with whisper.cpp. Returns the transcript text and optional segments. |
| analyze_transcriptA | Run a transcript through Jev (System One decision model) for structured analysis: topic membership, sentiment, sensitivity flags (profanity/violence/medical-advice/financial-advice/…), and a publish gate (publish / review / block). The agent gets typed labels, not prose. Always check |
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 4 tools
transcribe_media and transcribe_file overlap for local files, and transcribe_media already handles URL fetching that fetch_media also provides. However, the descriptions make each tool's primary role fairly clear: fetch_media is download-only, transcribe_file is local-only, and transcribe_media is the all-in-one path.
All tool names follow a consistent verb_noun snake_case pattern: transcribe_media, fetch_media, transcribe_file, analyze_transcript. The naming is predictable and easy to navigate.
Four tools is a reasonable size for this audio/video analysis pipeline. The count is only slightly higher than necessary because transcribe_file is mostly redundant with transcribe_media, but it is not bloated.
The server covers the full workflow: fetch media, transcribe it, and analyze the transcript with the Jev model. There are no obvious missing operations for the stated AV-analysis purpose.