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cerase-ai

cerase-media MCP

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Python
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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.

Tool

Question it answers

Returns

ocr

what is WRITTEN in this image?

{text, model}

describe_image

what does this image SHOW?

{description, model}

transcribe

what does this audio say?

{text, model}

analyze_ui

what's in this UI screenshot? — structured audit of layout, typography, colours, interactive elements, text, visual errors, accessibility, consistency

{analysis, model}

compare_screenshots

what changed between two screenshots? — before/after visual diff (layout / text / style / new / removed / regressions)

{diff, model}

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.

Env: LITELLM_BASE_URL, LITELLM_MASTER_KEY (scoped service key), CERASE_MULTIMODAL_ALIAS (default multimodal), CERASE_TOOL_WORKSPACE_ROOT (path-traversal guard root).

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maintenance

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