video-vision-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| JIRA_URL | No | Jira instance URL (optional, for analyzing Jira attachments). | |
| GROQ_API_KEY | No | Groq API key for tier 2 (cloud ASR). Optional, but if set, transcription uses Groq. | |
| JIRA_USERNAME | No | Jira username or email (optional). | |
| VIDEO_MCP_ENV | No | Path to a .env file to load environment variables. | |
| GEMINI_API_KEY | No | Gemini API key for tier 3 (native Gemini analysis). Optional, but if set, it becomes the default backend (unless disabled by VIDEO_MCP_DISABLE_GEMINI). | |
| JIRA_API_TOKEN | No | Jira API token (optional). | |
| OPENAI_API_KEY | No | OpenAI API key for tier 2 (cloud ASR). Optional, but if set, transcription uses OpenAI Whisper. | |
| VIDEO_MCP_CACHE_DIR | No | Override the default cache directory (~/.cache/video-vision-mcp/). | |
| VIDEO_MCP_WHISPER_MODEL | No | Whisper model to use for local transcription (tiny/base/small/medium/large-v3). Default: base. | |
| VIDEO_MCP_DISABLE_GEMINI | No | Set to 'true' to disable Gemini even if GEMINI_API_KEY is set, forcing tiers 1/2. | |
| VIDEO_MCP_WHISPER_MODEL_PATH | No | Path to a custom whisper model file (overrides VIDEO_MCP_WHISPER_MODEL). |
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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyze_videoA | Analyze a video into frames + transcript + metadata. Provide exactly ONE source:
frame_interval: seconds between sampled frames (default 1.0 = one per second). Denser sampling: 0.5 / 0.25 / 0.1; sparser: 2 / 5; or any custom value. The total is capped by the frame budget so long/dense videos can't flood context. Ignored by the native Gemini backend (it ingests the whole video). The backend (local whisper.cpp / OpenAI / Groq / native Gemini) is chosen automatically from configured keys and named in the result metadata. Results are cached per (file-hash, backend, frame_interval); pass force_refresh=true to recompute. |
| get_video_transcript_onlyC | Fast path: return only the transcript text (no frame images). Same inputs and backend selection as analyze_video. With the Gemini backend, returns Gemini's analysis text instead of a plain transcript. |
| extract_frames_atA | Extract frames at specific timestamps. timestamps accepts seconds ("12", "12.5") or "MM:SS" / "HH:MM:SS". Tier 1/2 return real frame images; with the Gemini backend you get a textual description of those moments instead (no local frame cutting). |
| list_recent_analysesA | List previously analyzed videos from the cache, with the backend used for each. |
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
Tools are generally distinct: analyze_video does full analysis, extract_frames_at targets specific timestamps, get_video_transcript_only is a fast path for transcript, and list_recent_analyses lists cache. However, get_video_transcript_only is a subset of analyze_video, which might cause some confusion for agents seeking only a transcript.
All tool names use a consistent snake_case verb_noun pattern (analyze_video, extract_frames_at, get_video_transcript_only, list_recent_analyses), making them predictable and easy to understand.
With 4 tools, the set is well-scoped for a video analysis server. Each tool serves a clear purpose without redundancy, and the number is neither too sparse nor excessive.
The tool surface covers core video analysis workflows: full analysis, targeted frame extraction, transcript retrieval, and cache listing. Minor gaps include lack of a delete cache tool or explicit backend configuration, but the set is functional for common use cases.