Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Description from the maker (original English)
Understand your videos with Reka AI — search, ask questions, and extract insights.
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This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"reka": {
"command": "uvx",
"args": [
"reka-mcp"
]
}
}
}Run this in your terminal
claude mcp add --transport http reka https://mcp.reka.ai/mcp --header 'X-Reka-API-Key: your-value'claude mcp add reka -- uvx reka-mcpFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"reka": {
"url": "https://mcp.reka.ai/mcp",
"headers": {
"X-Reka-API-Key": "your-value"
}
}
}
}Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"reka": {
"command": "uvx",
"args": [
"reka-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"reka": {
"type": "http",
"url": "https://mcp.reka.ai/mcp",
"headers": {
"X-Reka-API-Key": "your-value"
}
}
}
}Paste into .vscode/mcp.json in your project
{
"servers": {
"reka": {
"type": "stdio",
"command": "uvx",
"args": [
"reka-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
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X-Reka-API-Key secret required HTTP headerThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Tool names and descriptions exactly as the server reported them on Nov 10, 2026. The maker wrote them, not us; we show them as plain text.
upload_videoUpload a video from a URL. Returns a video_id. Local file paths are not accepted; upload files outside the MCP server and pass a reachable video_url. The upload runs asynchronously — poll get_video until status is 'uploaded', then call index_video to enable search and analysis.
list_videosList all videos in your account, or filter to a specific group by passing group_id. Shows upload status and which features have been indexed for each video. Each video's 'url' is a short-lived HTTPS presigned URL (expires within hours) — fetch immediately and do not store; call l
get_videoGet detailed information about a video including upload status, metadata (duration, resolution, fps), and per-feature indexing status. Use this to check if upload or indexing is complete. The 'url' field is a short-lived HTTPS presigned URL (expires within hours) — fetch immediat
update_videoUpdate a video's display name, title, description, or move it to a different group. At least one field must be provided. To remove a video from its group, pass group_id as null.
delete_videoPermanently delete a video and all its indexed data (transcript, captions, embeddings, etc.). This cannot be undone.
create_groupCreate a new video group. Groups organize videos into collections. Returns the new group's ID and name.
list_groupsList all video groups. Use list_videos with a group_id to see videos in a specific group.
delete_groupDelete a video group. Videos in the group are not deleted — they are simply removed from the group.
index_videoIndex a video for search, QA, or full analysis. Processes the video through a pipeline of AI features. Typically takes 3-7 minutes; longer for long videos or the 'full' pipeline. Times out after 10 minutes by default. Pipelines: - search_only: transcription + captions + embedding
search_videosFind WHEN and WHERE something happens across your videos. Returns timestamped results ranked by relevance — use these timestamps as start/end in ask_video for focused analysis. This is the recommended first step for most questions. Instead of asking ask_video about the entire vid
ask_video Looks like an instruction to the AIAsk a question about one or more videos with visual analysis. Most effective on focused time ranges — use start/end to specify the segment to analyze. BEFORE calling this tool, read the reka://docs/guide resource for recommended workflows. In most cases, you should first: - searc
segment_videoDetect objects in a video segment using text prompts. Describe what to look for and get per-frame detections with bounding boxes and confidence scores. Prompt tips: - Use broad, visual categories: 'animal', 'vehicle', 'person', 'text on screen' - Specific labels ('rabbit', 'Toyot
get_transcriptGet the spoken words in a video. Use this instead of ask_video when you need to read what was said — it returns the actual text, not a summary. Use start/end to narrow results for long videos. Requires the transcript feature to be indexed.
get_captionsGet AI-generated visual descriptions of what happens on screen. Use this to understand the visual content without watching — each caption describes a short segment with timestamps. Use start/end to narrow results. Requires the captions feature (qa_only or full pipeline).
get_scenesGet detected scene boundaries with start/end timestamps. Use this to understand the video's structure, then pass scene timestamps as start/end to: - ask_video for per-scene contextual analysis - segment_video to detect specific objects per scene (scenes typically fit in segment_v
get_feature_catalogList available video analysis features with their dependencies and descriptions. Use this to understand what features exist and what pipelines to use with index_video.
summarize_videoStart here. Get a compact overview of a video: metadata, which features are indexed, a transcript preview, and scene count. Use this to decide which tools to call next — then use segment_video to detect specific objects in time ranges of interest.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 10, 2026
The registry lists no source repo, so we cannot match the publisher (reka.ai) to the code
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Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 3 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
You need to fill in: X-Reka-API-Key
Either way works: install it on your computer or connect to the cloud version.
The GitHub repo last changed –, and the latest version (0.1.10) reached the registry 6 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.
In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server
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Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Let any LLM watch a video locally — and search everything it has ever watched.
Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.
No README found in the repo