Video Pipeline MCP Server
Integrates with FFmpeg for video processing operations such as trimming, merging, format conversion, and other video manipulations via an external MCP server.
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., "@Video Pipeline MCP Serverfind me all videos with 'tutorial' in the title from my workspace"
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.
Video Pipeline MCP Server
MCP server for managing a video pipeline. Works with Claude AI Web via a cloudflared tunnel.
Status (2026-07): a data management layer (files, tables/Excel, channel structure, search) + firewall + transport implemented. The product video pipeline (voiceover/transcription/images/editing) — under development. Plan for reaching corporate level:
docs/roadmap/. Legend below: ✅ implemented · 🟠 stub/partial · 🔲 planned.
Project structure (actual)
video_pipeline_mcp/
├── server.py ✅ точка входа: MCP-эндпоинт + туннель; регистрация инструментов
├── pyproject.toml ✅ сборка/зависимости (PEP 621) · requirements.lock — точные пины
├── install.sh · run.sh ✅ установка (.venv + cloudflared) · запуск (--tunnel)
│
├── config/ ✅ декларации (поведение, НЕ код)
│ │ (конфиг канала с S22 — не файл, а 7 листов channel_data.xlsx:
│ │ RESOURCE_LIMITS, WORKFLOW_SEQUENCES, PUBLISHING_SCHEDULE,
│ │ METADATA_DEFAULTS, AUTOMATION_RULES, SCENE_PROFILE, RENDER_CONFIG)
│ ├── server_reactions.yaml ✅ реестр реакций (коды → класс + recovery)
│ ├── firewall.yaml ✅ правила файрвола
│ ├── tunnel.yaml ✅ конфиг туннеля (gitignored — секрет)
│ └── templates/
│ ├── workspace/ ✅ 6 шаблонов: niche/network/channel/video/competitor_channel/competitor_video
│ └── tables/ 🔲 схемы книг (*.schema.yaml) — воркстрим A1′
│
├── core/ — ядро (бизнес-логика)
│ ├── contracts/ ✅ Pydantic: ToolResult, ErrorDetail, Fact, TaskStatus
│ ├── engine/ ✅ generic-движок + template_engine
│ ├── firewall/ ✅ файрвол + rules/ (rate_limit, injection, ip_blocklist, anomaly)
│ ├── state/ ✅ read.json / write.json / session log
│ ├── reactions/ ✅ читалка server_reactions
│ ├── ids/ ✅ генерация ID + link_registry
│ ├── tables/ · excel/ ✅ слой таблиц и Excel-книг
│ ├── search/ ✅ поиск по ФС/таблицам (FsSearcher + QueryPlanner)
│ ├── transport/ ✅ туннель к Claude (transport + tunnel)
│ ├── runner/ ✅ инференс в отдельном процессе: сервис на петле + супервизор
│ ├── paths.py ✅ containment путей в workspace/
│ └── providers/ 🟠 адаптеры провайдеров (заглушки — в разработке)
│ ├── stt/ (stable-ts) · tts/ (LiteLLM) · img/ (LiteLLM) · ffmpeg/ (внешний MCP)
│
├── tools/ 🔲 тонкие обёртки Bounded Context (план: вынос из server.py, A2)
├── pipeline/ 🔲 оркестрация процессов (entry_points + steps) — план
├── scripts/ 🔲 утилиты (план: introspect_tables.py — A1′)
├── docker/ ✅ образ раннера (среда инференса заперта в контейнере)
├── tests/ ✅ quick/ (unit) + симуляции (virus/bot_army/cache_*/…)
└── docs/
├── dev/ — история файлов, спеки (gitignored)
└── roadmap/ ✅ план развития до корпоративного уровня + канон спек (spec/)Tools currently (52 in tools/list): ✅ fs_* (files), table_*/json_* (tables),
excel_* (workbooks), structure_* (creating channels/videos by templates), search_* (search);
🟠 tts_*/stt_*/img_*/video (media — stubs).
Related MCP server: Video MCP
Installation
./install.sh # .venv + БАЗОВЫЕ зависимости + cloudflared
./install.sh --media # + облачные/HTTP-провайдеры и ONNX (bg_removal/upscale)
./install.sh --local # + локальный ML-инференс (torch/stable-ts/piper/diffusers)
./install.sh --gpu-amd # + локальный инференс на карте AMD (rocm-сборка torch)Savings: the base installs without heavy libraries (torch ~3 GB, stable-ts, litellm, onnxruntime) —
they are imported lazily and placed in optional groups. The server runs on a clean base, and
media tools honestly respond with PROVIDER_NOT_CONFIGURED / LOCAL_INFERENCE_FAILED.
Running
./run.sh # сервер + туннель (--no-tunnel — только локально)Dependencies
Source of truth — pyproject.toml (pip install -e .); exact base pins — requirements.lock;
dev tools — pip install -e ".[dev]"; media — pip install -e ".[media]"; local
ML inference — pip install -e ".[local]" (and pip install -e ".[gpu-amd]" for AMD cards).
Base (mandatory, ~50 MB)
Package | Purpose |
pydantic | contracts (ToolResult, ErrorDetail) |
openpyxl | Excel work |
pyyaml · jsonschema | reading configs · validating params by schema |
aiohttp | HTTP transport of server behind tunnel |
cryptography | artifact signing (S9), instance key |
Media (on demand, .[media])
Package | Purpose |
httpx | HTTP client to cloud provider gateways |
litellm | unified access to online models (STT/TTS/IMG) |
onnxruntime · pillow | background removal, upscale (ONNX graph) |
ffmpeg-python | video operations |
Local (on demand, .[local] — gigabytes of runtime)
Package | Purpose |
torch · torchaudio | local inference runtime |
stable-ts | local transcription (whisper) |
piper-tts · diffusers · transformers | voiceover, image generation |
cloudflared — binary (not pip), installed via install.sh.
Development
Development plan, findings, and spec canon: docs/roadmap/.
Suggestions for improving functions: docs/roadmap/spec/IMPROVEMENTS.md.
This server cannot be deployed
Maintenance
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