vidlizer pulls frames out of any video, image, or PDF using ffmpeg, sends them to a vision LLM, and returns a `flow` array — one entry per scene.
Vidlizer MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. vidlizer pulls frames out of any video, image, or PDF using ffmpeg, sends them to a vision LLM, and returns a flow array — one entry per scene. Each entry tells you what happened, who was on screen, what text was visible, and what.
The server ships on PyPI as vidlizer, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Once Vidlizer is connected, these are the calls the assistant has available:
Subcommands — The Subcommands tool exposed by this serverYou will need 8 environment variables: PROVIDER, OLLAMA_HOST, OLLAMA_MODEL, OPENROUTER_API_KEY, OPENROUTER_MODEL, OPENAI_BASE_URL, OPENAI_API_KEY, OPENAI_MODEL. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Vidlizer's toolset — Subcommands — is a fair guide to whether it matches your workflow. It is maintained by arizawan; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Subcommands | The Subcommands tool exposed by this server. |
{
"mcpServers": {
"vidlizer": {
"command": "uvx",
"args": ["vidlizer"],
"env": {
"PROVIDER": "your-value",
"OLLAMA_HOST": "your-value",
"OLLAMA_MODEL": "your-value",
"OPENROUTER_API_KEY": "your-value",
"OPENROUTER_MODEL": "your-value",
"OPENAI_BASE_URL": "your-value",
"OPENAI_API_KEY": "your-value",
"OPENAI_MODEL": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| PROVIDER | Configuration value read at startup. | Optional |
| OLLAMA_HOST | Endpoint or connection string the server talks to. | Optional |
| OLLAMA_MODEL | Configuration value read at startup. | Optional |
| OPENROUTER_API_KEY | Credential the server authenticates with. | Yes |
| OPENROUTER_MODEL | Configuration value read at startup. | Optional |
| OPENAI_BASE_URL | Endpoint or connection string the server talks to. | Yes |
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| OPENAI_MODEL | Configuration value read at startup. | Optional |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.