Integrates with ElevenLabs API for high-fidelity word-level transcription mapping during video ingestion.
Leverages FFmpeg for advanced video/audio processing including EDL cuts, overlay rendering, subtitle burning, LUT color grading, and AI audio restoration.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@omni-video-mcp ingest all .mp4 files for editing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
The Omni-Video Studio MCP is an enterprise-grade, autonomous Model Context Protocol (MCP) server that empowers any LLM-enabled IDE (Cursor, Claude Code, Antigravity) to act as a professional video editor.
It evolves simple transcription-based editing into a deterministic, token-efficient, and pipeline-driven workflow, featuring agent-native motion graphics (Hyperframes), visual metadata proxies, and high-fidelity final renders.
Metadata Proxy Ingestion: Instead of streaming expensive video tokens to an LLM, this server pre-processes footage to extract a takes_packed.md (audio mapping) and a Visual Scene Graph. The agent edits using text proxies, cutting costs and accelerating reasoning.
Hyperframes Engine: Forget complex Node.js dependencies (e.g., Remotion). The agent generates deterministic HTML/CSS motion graphics which are instantly rendered to transparent video using Playwright.
Advanced Rendering Pipeline: Powered by robust FFmpeg filter graphs, the final output supports EDL (Edit Decision List) cuts, overlay rendering, Subtitle burning, LUT color grading, and optional DeepFilterNet AI audio restoration.
IDE-Agnostic: Because it adheres to the official MCP specification, it drops directly into Cursor, Antigravity, or Claude Desktop without custom plugins.
Related MCP server: Video MCP
Prerequisites:
python 3.10+
ffmpeg (must be installed on your system path)
uv (recommended for dependency management)
# Clone the repository
git clone https://github.com/your-org/omni-video-mcp.git
cd omni-video-mcp
# Install dependencies
uv venv
source .venv/bin/activate
uv pip install -e .
# Install Playwright browsers (for Hyperframes)
playwright install chromiumAdd the server to your IDE's MCP settings file (e.g., ~/.gemini/antigravity/mcp_config.json, ~/.cursor/mcp.json, or Claude Desktop config):
{
"mcpServers": {
"omni-video-mcp": {
"command": "uv",
"args": [
"run",
"/path/to/omni-video-mcp/server.py"
],
"env": {
"ELEVENLABS_API_KEY": "your_api_key_here"
}
}
}
}Note: The ELEVENLABS_API_KEY is currently required for high-fidelity word-level transcription mapping during ingestion.
When the agent uses this MCP server, it follows a 4-phase architecture:
Phase 1: Ingestion (omni_video_ingest)
The agent scans your raw .mp4 / .mov files, extracting a packed markdown transcript and an initial Visual Scene Graph.
Phase 2: Director's Cut (omni_video_preview)
The agent uses the transcript to construct an EDL (Edit Decision List) of the best takes. Ambiguous cuts can be visually verified by generating filmstrip PNGs via the preview tool.
Phase 3: VFX (omni_video_generate_vfx)
The agent generates HTML/CSS motion graphics (lower thirds, b-roll layouts) and the server renders them deterministically into transparent .webm videos via Hyperframes.
Phase 4: Sweetening & Render (omni_video_render)
The agent passes the EDL, VFX timestamps, and render settings to the server, which builds a complex FFmpeg graph to concatenate the footage, grade it, restore the audio, and export a final master.
Contributions are welcome! If you're adding new render pipeline capabilities (like auto-tracking or local whisper fallbacks), please open a PR. Ensure that any added Python dependencies are added to the pyproject.toml using uv add <package>.
MIT License
Renders motion graphics (e.g., lower thirds, titles) using Hyperframes. Returns the path to the rendered transparent .mov or .webm file.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It states the tool renders transparent videos using Hyperframes and returns a file path, but fails to mention whether it is synchronous or async, any size limits, error conditions, or side effects. The minimal detail leaves the agent guessing about important behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences covering the core action and output. It front-loads the primary purpose and wastes no words. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and an output schema that likely exists but is not described, the tool description is incomplete. It does not explain the return format beyond a file path, nor does it cover prerequisites for HTML/CSS validity or duration limits. The agent may need to infer or experiment to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has a single parameter 'request' with nested properties (html_content, css_content, duration_seconds), all with schema descriptions. However, the context indicates 0% schema description coverage, likely because the top-level parameter lacks a description. The tool description itself does not mention any parameters or add meaning beyond what the schema provides. For parameter understanding, the agent gets no extra help from the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool renders motion graphics (lower thirds, titles) using Hyperframes and returns the path to a transparent .mov or .webm file. The verb 'renders' and specific resource 'motion graphics' distinguish it from sibling tools like omni_video_ingest, omni_video_preview, and omni_video_render.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor when not to use it. It does not mention any prerequisites or context for invoking it. With sibling tools listed but no differentiation, the agent lacks contextual cues for proper selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Ingests a directory of video files, generates word-level audio transcripts, and constructs a semantic Visual Scene Graph for B-Roll searching. Returns the path to the generated project metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the transparency burden. It discloses that transcripts and scene graphs are generated and a metadata path is returned, but does not mention whether ingestion modifies source files, requires permissions, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the main action, and every sentence provides essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is non-trivial (multiple stages), but the output schema exists to clarify return values. The description covers the main inputs and outputs, though it lacks edge-case or error details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for the top-level parameter. The tool description adds that 'directory_path' is a directory of video files, but the schema already includes a similar description for 'directory_path'. Thus, the description adds minimal value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs: 'ingests', 'generates word-level audio transcripts', 'constructs a semantic Visual Scene Graph'. It distinguishes well from sibling tools which handle VFX, preview, and render.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied (ingesting video files for transcript and scene graph generation), but no explicit guidance on when to use versus alternatives or prerequisites is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generates a filmstrip PNG of the specified video segment. Useful for visually verifying cut boundaries or B-roll placement. Returns the absolute path to the generated PNG file.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool generates a PNG and returns its path, implying a read-only operation. However, it does not detail any side effects, permissions, or safety implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, each serving a distinct purpose: what it does, when to use it, and what it returns. No wasted words, front-loaded with core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the relatively simple operation and the presence of a nested parameter schema with descriptions, plus an output schema (not shown but indicated), the description adequately covers the tool's purpose and return value. It could mention the parameter structure briefly, but the schema covers that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the tool description does not explain any parameters. While the schema itself contains descriptions for file_path, start_time, and end_time, the description adds no additional meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Generates a filmstrip PNG of the specified video segment,' which is a specific verb-resource combination. It distinguishes from siblings (generate_vfx, ingest, render) by focusing on preview.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases: 'visually verifying cut boundaries or B-roll placement.' It does not mention when not to use or alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Orchestrates the final render pipeline: applies EDL cuts, overlay graphics, LUT color grading, audio restoration, and subtitle burning. Returns the path to the final rendered video.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries behavioral disclosure. It accurately describes the tool's actions (applying cuts, LUT, etc.) and the return value (path to final video). It does not mention side effects like file creation or potential errors, but the output_path parameter implies file writing. This is adequate but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently lists all main operations and the return value. It is front-loaded with the core purpose ('Orchestrates the final render pipeline') and contains no fluff or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's core functionality and return value, and the schema outlines required parameters. It lacks details about error handling or prerequisites, but given the presence of an output schema (implied) and sibling context, it is mostly complete for a final render step.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema properties have detailed descriptions (e.g., edl_json, lut_path, output_path), which already clarify parameter meanings. The tool description does not add additional parameter information beyond listing the tool's capabilities. With good schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool orchestrates the final render pipeline and enumerates specific operations (EDL cuts, overlay graphics, LUT grading, audio restoration, subtitle burning). It distinguishes from siblings through the term 'final render,' differentiating it from ingest, VFX generation, and preview tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is the last step in a video processing pipeline, but does not explicitly state when to use it versus alternatives or mention prerequisites. The mention of 'final render' provides some context, but more direct guidance would improve this dimension.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0omni_video_generate_vfxomni_video_ingestomni_video_previewomni_video_renderScored across 4 tools
Each tool targets a distinct stage of the video pipeline: ingest, VFX generation, preview verification, and final render. There is no overlap in functionality.
All tools follow the consistent pattern 'omni_video_<verb>', using clear action verbs (ingest, generate_vfx, preview, render) that accurately describe their purpose.
With 4 tools, the server is well-scoped for a video processing pipeline. Each tool serves a critical, non-redundant role, making the set neither too sparse nor too heavy.
The pipeline covers ingest, VFX, preview, and render, but lacks independent tools for editing (e.g., trimming) or managing overlays directly. However, the render tool bundles many operations, minimizing gaps.
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