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)
Create and manage Convertrilo video encoding jobs from MCP clients and agent workflows.
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Pick the AI app you use, then copy the settings.
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"convertrilo": {
"command": "npx",
"args": [
"-y",
"@convertrilo/sdk"
]
}
}
}Run this in your terminal
claude mcp add convertrilo -- npx -y @convertrilo/sdkPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"convertrilo": {
"command": "npx",
"args": [
"-y",
"@convertrilo/sdk"
]
}
}
}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": {
"convertrilo": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@convertrilo/sdk"
]
}
}
}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.
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Runs on your computer; the tool list appears in your AI app after you install it.
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 6, 2026
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This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
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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 1 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.
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It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.1.2) reached the registry 4 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server
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Reading a card:★ 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.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Type-safe client for the Convertrilo API.
When optimize: "vmaf" is requested, Convertrilo performs VMAF sampling and one optimized final encode. A supplied passes: 2 value remains accepted for backward compatibility, but the API uses and bills one effective pass.
Pricing responses expose requestedPasses and effectivePasses. New integrations should use passes: 1 with VMAF.
pnpm add @convertrilo/sdk
The package currently targets modern Node.js runtimes with global fetch. If your runtime does not provide fetch, pass fetchImpl to the client.
The package includes two executable automation tools:
export CONVERTRILO_API_KEY="cvr_..."
convertrilo encode https://example.com/input.mp4 \ --codec h264 \ --resolution 1080p \ --audio-policy transcode-aac \ --frame-rate-policy cap \ --scale-policy no-upscale \ --quality better \ --wait \ --json
convertrilo-mcp
Use convertrilo for scripts, CI, cron, and local operations. Use convertrilo-mcp as a stdio MCP server for agent and workflow integrations.
Run convertrilo login or convertrilo init to save an API key locally, or run convertrilo with no arguments to start the interactive encode wizard. Waiting commands show terminal progress by default and keep --json clean for scripts.
Shell completion snippets are available with:
convertrilo completion zsh
See docs/CLI-AND-MCP.md for commands, MCP client config, S3 output examples, VMAF, two-pass, status, cancel, and balance workflows.
If you want the simplest terminal walkthrough, start with docs/CLI-QUICKSTART.md.
For agent/client setup, use docs/MCP-QUICKSTART.md.
MCP registry metadata lives in server.json.
import { ConvertriloClient } from "@convertrilo/sdk";
const client = new ConvertriloClient({ baseUrl: "https://api.convertrilo.com", apiKey: process.env.CONVERTRILO_API_KEY, });
Use an API key for server-to-server integrations. Browser apps should call your own backend, then your backend calls Convertrilo.
The examples/ directory contains starter scripts for the main integration paths:
node-url-to-cdn.ts - encode a public URL and receive a signed CDN download URLnode-url-to-s3.ts - encode a public URL and upload the result to S3/S3-compatible storagegoogle-drive-byo-token.ts - upload output to Google Drive using a customer-owned service accountfolder-ingest-s3.ts - queue one encode job per video in an S3 prefixidempotency.ts - safely retry createJob and createJobsBulkwebhook-receiver-hmac.ts - verify managed webhook HMAC signatures from a Node receiverLocal SDK smoke tests use .env, but published SDK users should provide credentials through their own server environment. Do not put Convertrilo API keys or customer storage tokens in frontend code.
For a complete server-to-server walkthrough covering URL, S3, folder ingest, Google Drive service accounts, polling, and webhooks, see docs/API-INTEGRATION-GUIDE.md.
Use an idempotency key when retrying create calls from your backend. Reusing the same key with the same body returns the original response instead of creating duplicate jobs.
const job = await client.createJob({
externalId: "upload-123", metadata: { customerId: "cus_123" }, codec: "h264", resolution: "1080p", fps: 30, }, { idempotencyKey: "job-upload-123", });
const batch = await client.createJobsBulk({ jobs: [ { externalId: "batch-42:clip-1", codec: "h264", resolution: "1080p", fps: 30, sourceS3: { bucket: "source", key: "clip-1.mp4" }, }, ], settings: { confirm: true }, }, { idempotencyKey: "bulk-batch-42", });
Delete managed upload and output objects after a job reaches a terminal state:
await client.cancelJob(jobId); // required first for created, queued, or running jobs
const result = await client.deleteJobFiles(jobId); console.log(result.objectsDeleted, result.jobRetained);
The job record and billing history remain available for auditing. Active jobs return the stable job_active error until they are canceled.
Save reusable encode settings without storing source URLs, storage credentials, or output secrets:
const preset = await client.createEncodePreset({
name: "Default 1080p web MP4", settings: { codec: "h264", resolution: "1080p", fps: 30, preset: "standard", bitrateTier: "medium", passes: 1, policy: "fastest", container: "mp4", quality: "better", optimize: "none", vmafTarget: 93, audioPolicy: "transcode-aac", frameRatePolicy: "cap", scalePolicy: "no-upscale", }, });
const { presets } = await client.getEncodePresets();
Save reusable delivery targets without storing raw storage secrets in the destination itself. S3 destinations reference an encrypted saved S3 credential:
const destination = await client.createOutputDestination({
name: "Customer uploads bucket", config: { type: "s3", credentialId: "0f5a7f2b-4ff2-45d4-b76f-1f7b6e98d4d1", keyPrefix: "processed/", }, });
const { destinations } = await client.getOutputDestinations();
const job = await client.onDemandEncode({
sourceUrl: "https://example.com/input.mp4", externalId: "customer-video-123", metadata: { customerId: "cus_123", workflow: "daily-compression", }, codec: "h264", resolution: "1080p", quality: "better", audioPolicy: "transcode-aac", frameRatePolicy: "cap", scalePolicy: "no-upscale", }, { idempotencyKey: "encode-customer-video-123", });
let finalStatus; while (true) { finalStatus = await client.onDemandStatus(job.jobId);
if (finalStatus.status === "success") break; if (finalStatus.status === "failed") { throw new Error(finalStatus.failureMessage || "Encoding failed"); }
await new Promise((resolve) => setTimeout(resolve, 5000)); }
console.log(finalStatus.downloadUrl); console.log(finalStatus.requestedExecution); console.log(finalStatus.effectiveExecution); console.log(finalStatus.sourceProbe?.color); console.log(finalStatus.outputProbe);
Terminal users can inspect the same report with:
convertrilo status JOB_ID --json
convertrilo wait JOB_ID --json
const job = await client.onDemandEncode({
sourceUrl: "https://example.com/input.mp4", codec: "h264", resolution: "1080p", outputS3: { bucket: "customer-output-bucket", key: "encoded/input-1080p.mp4", region: "us-east-1", accessKeyId: process.env.CUSTOMER_S3_ACCESS_KEY_ID, secretAccessKey: process.env.CUSTOMER_S3_SECRET_ACCESS_KEY, }, });
console.log(job.jobId);
For S3-compatible services, pass endpoint and usually forcePathStyle: true.
For headless API integrations, save a customer-owned Google service account once. Use a Google Shared Drive for output and add the returned service-account email as a member with permission to create files.
const credential = await client.createGoogleDriveCredential({
name: "Production Drive", serviceAccount: JSON.parse(process.env.GOOGLE_SERVICE_ACCOUNT_JSON!), });
const job = await client.onDemandEncode({ sourceUrl: "https://example.com/input.mp4", codec: "h264", resolution: "1080p", outputGoogleDrive: { folderId: "GOOGLE_DRIVE_FOLDER_ID", fileName: "input-1080p.mp4", credentialId: credential.id, }, });
console.log(job.jobId);
Dashboard Google Picker authorization is separate from SDK automation.
Queue one job per video in an S3 prefix:
const batch = await client.onDemandIngestFolder({
externalIdPrefix: "batch-2026-06-09", metadata: { customerId: "cus_123", workflow: "folder-compression", }, sourceS3: { bucket: "customer-source-bucket", prefix: "incoming/", region: "us-east-1", accessKeyId: process.env.CUSTOMER_S3_ACCESS_KEY_ID, secretAccessKey: process.env.CUSTOMER_S3_SECRET_ACCESS_KEY, }, outputDestination: "s3", outputS3: { bucket: "customer-output-bucket", prefix: "encoded/", region: "us-east-1", accessKeyId: process.env.CUSTOMER_S3_ACCESS_KEY_ID, secretAccessKey: process.env.CUSTOMER_S3_SECRET_ACCESS_KEY, }, codec: "h264", maxFiles: 25, resolution: "1080p", }, { idempotencyKey: "folder-batch-2026-06-09", });
for (const job of batch.jobs || []) { console.log(job.jobId, job.externalId, job.fileName); }
Use maxFiles to cap how many discovered videos are queued from a folder.
Queue one job per video in a Google Drive folder:
const batch = await client.onDemandIngestFolder({
sourceGoogleDrive: { folderId: "SOURCE_FOLDER_ID", credentialId: credential.id, }, outputDestination: "google-drive", outputGoogleDrive: { folderId: "OUTPUT_FOLDER_ID", credentialId: credential.id, }, codec: "h264", maxFiles: 25, resolution: "1080p", });
for (const job of batch.jobs || []) { console.log(job.jobId, job.fileName); }
Convertrilo mints short-lived Google tokens from the encrypted service-account credential when each worker starts.
Poll each returned jobId with client.onDemandStatus(jobId).
Managed webhooks are HMAC signed. You can test a webhook and inspect recent delivery attempts:
await client.testWebhook(webhookId);
const history = await client.getWebhookDeliveries(webhookId); for (const delivery of history.deliveries || []) { console.log(delivery.status, delivery.statusCode, delivery.event); }
The SDK types are generated from openapi.yaml.
pnpm run generate
pnpm run build
The generate script uses --default-non-nullable false so OpenAPI defaults remain optional in TypeScript request bodies.