mcp-adf

mcp-adf

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.

Category
访问服务器

README

mcp-adf — Azure Data Factory MCP server

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.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选
mcp-server-qdrant

mcp-server-qdrant

这个仓库展示了如何为向量搜索引擎 Qdrant 创建一个 MCP (Managed Control Plane) 服务器的示例。

官方
精选
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选