cerase-media MCP

First-party multimodal understanding (M-MEDIA-1 = the merge of the former cerase-ocr + cerase-transcriber): five async tools over the multimodal tool-model alias through cerase-litellm, billed per-agent. The last two (analyze_ui, compare_screenshots) are the UX/UI screenshot pair added by M-CERASE-MEDIA-UX — same multimodal endpoint, specialised prompts, no extra dependency.

Image input is accepted three ways (pick one): path (a file under CERASE_TOOL_WORKSPACE_ROOT), image_url, or image_base64. compare_screenshots takes the two-image variants (path1/image1_url/ image1_base64 and path2/…).

Async by design: the tools are ~100% LLM-wait, so concurrent requests run on parallel I/O lanes inside the single runner container (no per-modality queue). ffmpeg (audio normalisation) runs as an async subprocess.

Long audio: the chunker

transcribe cuts anything longer than a chunk (chunker.py), transcribes the pieces concurrently and re-assembles the text. Each piece after the first repeats the previous one's last seconds so a word cannot be lost on a cut, and the repeated words are located and dropped when the pieces are joined. A caller that knows where the speakers change can hand over a speaker timeline: the cuts move onto the turn changes, where nothing needs repeating.

Every piece — including a recording short enough to need no cut — gets a second of silence in front of it. Audio that starts on a word comes back with that first sentence missing.

Related MCP server: mcp-multimedia-server

The same code as an HTTP endpoint

transcription_api.py serves POST /v1/audio/transcriptions — the interface meeting drivers already speak — over the same chunker. The compose service cerase-transcription runs this image on that entrypoint:

python -m uvicorn --app-dir /app --factory transcription_api:create_app --host 0.0.0.0 --port 8080

It takes the OpenAI fields (file, model, language, response_format, stream) plus two of its own: agent_id (or the X-Cerase-Agent-Id header), which every model call is billed to and which is required, and speaker_timeline, a JSON array of {start, end, speaker}. Callers present CERASE_INTERNAL_SECRET as a bearer; an unset secret refuses every request. With stream=true each piece leaves as a transcript.text.delta event as soon as it lands, and the run ends with transcript.text.done.

Env: LITELLM_BASE_URL, LITELLM_MASTER_KEY (scoped service key), CERASE_MULTIMODAL_ALIAS (default multimodal), CERASE_TOOL_WORKSPACE_ROOT (path-traversal guard root), CERASE_INTERNAL_SECRET (the HTTP endpoint's bearer).