# HLS / IPTV MCP Server — AI Video Streaming (`obedient_hierarchy/hls-mcp-server`) Actor MCP server for video streaming workflows. Convert videos to HLS, transcode, create thumbnail strips, check stream health, compress, trim, watermark, create GIFs, extract audio, and more — all via natural language. Powered by ffmpeg. - **URL**: https://apify.com/obedient\_hierarchy/hls-mcp-server.md - **Developed by:** [Kritsana](https://apify.com/obedient_hierarchy) (community) - **Categories:** Automation, Developer tools, Videos - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## HLS / IPTV MCP Server MCP server for video streaming — AI controls ffmpeg to create HLS streams, transcode, generate thumbnails, compress videos, and more. ### Quick Start ```bash pip install -e . python -m hls_mcp_server --transport sse --port 8000 ```` #### Apify Actor ```bash apify push ``` ### Tools #### HLS Streaming | Tool | Description | |---|---| | `video_to_hls` | Convert video to multi-bitrate HLS (.m3u8 + .ts) | | `hls_to_mp4` | Convert HLS stream back to MP4 | | `generate_master_playlist` | Create master playlist from variant definitions | | `stream_health_check` | Check HLS stream availability and segment health | #### Thumbnails | Tool | Description | |---|---| | `create_thumbnail_strip` | Generate thumbnail sprite sheet | | `extract_thumbnail` | Capture single frame at timestamp | #### Processing | Tool | Description | |---|---| | `video_metadata` | Get codec, resolution, bitrate, duration, audio tracks | | `compress_video` | Compress video (CRF-based) | | `trim_video` | Cut a segment | | `change_resolution` | Resize/scale video | | `add_watermark` | Overlay text watermark | | `concat_videos` | Merge multiple videos | | `create_gif` | Convert clip to animated GIF | | `extract_audio` | Extract audio track (mp3/aac/wav/ogg/flac) | | `live_transcode_status` | Server capabilities and encoder availability | ### Environment Variables | Variable | Default | Description | |---|---|---| | `MCP_TRANSPORT` | `stdio` | `stdio` or `sse` | | `MCP_PORT` | `8000` | Port for SSE | ### Claude Desktop Config ```json { "mcpServers": { "hls": { "command": "python", "args": ["-m", "hls_mcp_server"], "env": {} } } } ``` ### Example ``` User: Convert video.mp4 to HLS with 1080p, 720p, 480p variants AI: → video_to_hls("video.mp4", "/output", resolutions="1080,720,480") ✅ Master playlist: /output/master.m3u8 3 variants, 45 segments generated User: Make a thumbnail strip every 10 seconds AI: → create_thumbnail_strip("video.mp4", "/output/strip.jpg", interval=10) ✅ Thumbnails saved: /output/strip.jpg (120 KB) ``` ### Requirements - ffmpeg (auto-installed in Docker image) - Python 3.11+ ### License MIT # Actor input Schema ## `mcpTransport` (type: `string`): SSE for remote AI clients. Stdio for local use. ## `mcpPort` (type: `integer`): Port for SSE transport. ## Actor input object example ```json { "mcpTransport": "sse", "mcpPort": 8000 } ``` # Actor output Schema # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("obedient_hierarchy/hls-mcp-server").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("obedient_hierarchy/hls-mcp-server").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call obedient_hierarchy/hls-mcp-server --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=obedient_hierarchy/hls-mcp-server", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "HLS / IPTV MCP Server — AI Video Streaming", "description": "MCP server for video streaming workflows. Convert videos to HLS, transcode, create thumbnail strips, check stream health, compress, trim, watermark, create GIFs, extract audio, and more — all via natural language. Powered by ffmpeg.", "version": "1.0", "x-build-id": "sTrsFU0OlyypOuB0s" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/obedient_hierarchy~hls-mcp-server/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-obedient_hierarchy-hls-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/obedient_hierarchy~hls-mcp-server/runs": { "post": { "operationId": "runs-sync-obedient_hierarchy-hls-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/obedient_hierarchy~hls-mcp-server/run-sync": { "post": { "operationId": "run-sync-obedient_hierarchy-hls-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mcpTransport": { "title": "MCP Transport", "enum": [ "sse", "stdio" ], "type": "string", "description": "SSE for remote AI clients. Stdio for local use.", "default": "sse" }, "mcpPort": { "title": "MCP Port", "minimum": 1024, "maximum": 65535, "type": "integer", "description": "Port for SSE transport.", "default": 8000 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```