Command Palette

Search for a command to run...

UnylyUnyly
Весь каталог

Adf

БесплатноНе проверен

Enables interaction with Azure Data Factory instances, allowing users to list, read, create, update, and trigger pipelines, datasets, linked services, and runs

GitHubEmbed

Описание

Enables interaction with Azure Data Factory instances, allowing users to list, read, create, update, and trigger pipelines, datasets, linked services, and runs through natural language.

README

One FastMCP server fronting one or more Azure Data Factory instances. Every tool takes a factory argument naming a target in connections.json; call list_factories first to see the configured targets.

Built on azure-mgmt-datafactory + azure-identity. Read tools work on any target; tools that create resources or trigger runs require the target to be flagged "writable": true.

Tools

Discovery / read (any target):

  • list_factories — configured targets
  • discover_factories — every ADF in a target's subscription (to fill in config)
  • list_pipelines / get_pipeline
  • list_datasets / get_dataset
  • list_linked_services / get_linked_service
  • list_triggers / get_trigger

Run monitoring / error analysis (any target):

  • list_pipeline_runs — run history over the last N days; filter by pipeline/status
  • get_pipeline_run
  • list_activity_runs — per-activity status / timing / error for a run
  • analyze_run_errors — run message + every failed activity's error code & message

Write (only against a "writable": true target):

  • create_or_update_linked_service
  • create_or_update_dataset
  • create_or_update_pipeline
  • run_pipeline — trigger a run, returns the run_id
  • cancel_pipeline_run
  • start_trigger / stop_trigger

Resource definitions are the JSON you see in ADF Studio's code view — pass either the full { "name": ..., "properties": {...} } object or just the inner properties object.

Configure

Copy connections.example.json to connections.json and fill in your targets:

{
  "dev": {
    "subscription_id": "00000000-0000-0000-0000-000000000000",
    "resource_group": "rg-data-dev",
    "factory_name": "adf-dev",
    "auth": "azure-cli",
    "writable": true
  },
  "prod": {
    "subscription_id": "00000000-0000-0000-0000-000000000000",
    "resource_group": "rg-data-prod",
    "factory_name": "adf-prod",
    "auth": "azure-cli",
    "writable": false
  }
}

connections.json and .env are gitignored — they never leave your machine.

Auth

Set "auth" per target:

value how it signs in
azure-cli (default) reuses an az login token (requires Azure CLI)
broker Windows WAM broker popup (no CLI needed; great in tenants that block device-code flow)
interactive browser sign-in popup
service-principal app registration; secret read from client_secret_env (see .env.example)
default DefaultAzureCredential (env → cli → broker → …)

The identity needs an ADF RBAC role on the factory — Data Factory Contributor for create/trigger, Reader for the read tools.

Setup

python -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements.txt
copy connections.example.json connections.json   # then edit it
.\.venv\Scripts\python.exe server.py              # smoke test (Ctrl+C to stop)

Finding your factories

discover.py signs in once and lists every subscription and the data factories in each, so you can fill in subscription_id / resource_group / factory_name:

.\.venv\Scripts\python.exe -m pip install azure-mgmt-subscription azure-mgmt-resource
.\.venv\Scripts\python.exe discover.py

Register with an MCP client

See examples/mcp.json:

{
  "mcpServers": {
    "adf": {
      "command": "C:\\path\\to\\mcp-adf\\.venv\\Scripts\\python.exe",
      "args": ["C:\\path\\to\\mcp-adf\\server.py"],
      "env": {}
    }
  }
}

Use with Claude Desktop

Claude Desktop reads its MCP servers from claude_desktop_config.json. Open it from Settings → Developer → Edit Config (this creates the file if it doesn't exist), or edit it directly:

  • Windows: %APPDATA%\Claude\claude_desktop_config.json
  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Add this server under mcpServers, using absolute paths to the venv's Python and server.py:

{
  "mcpServers": {
    "adf": {
      "command": "C:\\path\\to\\mcp-adf\\.venv\\Scripts\\python.exe",
      "args": ["C:\\path\\to\\mcp-adf\\server.py"],
      "env": {}
    }
  }
}

On macOS the paths are POSIX, e.g. "command": "/Users/you/mcp-adf/.venv/bin/python". Save the file and fully quit and reopen Claude Desktop (use Quit from the tray/menu-bar icon — closing the window isn't enough). The server's tools then appear in the tools (🔌) menu of a new chat.

License

MIT — see LICENSE.

from github.com/rajivdatta/mcp-adf

Установка Adf

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/rajivdatta/mcp-adf

FAQ

Adf MCP бесплатный?

Да, Adf MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Adf?

Нет, Adf работает без API-ключей и переменных окружения.

Adf — hosted или self-hosted?

Доступен hosted-вариант: Unyly запускает сервер в облаке, локальная установка не обязательна.

Как установить Adf в Claude Desktop, Claude Code или Cursor?

Открой Adf на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

Похожие MCP

Compare Adf with

Не уверен что выбрать?

Найди свой стек за 60 секунд

Автор?

Embed-бейдж для README

Похожее

Все в категории development