Azure Data Factory の監視と根本原因分析を行うための読み取り専用 MCP (Model Context Protocol) サーバーです。VS Code / Claude Code から使用するために構築されています。
ステータス: Step 1 (スケルトン + ヘルスチェック)。 まだ Azure との接続はありません。これは Step 2 (auth) と Step 3 (ADF tools) で追加されます。
Python 3.11+
Related MCP server: azure-adf-mcp
cd adf-mcp-server
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -e ".[dev]"
cp .env.example .env現時点では .env に必要なのはサーバー設定ブロック (ADF_MCP_*) だけです。AZURE_* の行は Step 2 用のプレースホルダーなので、今はコメントアウトしたままにしてください。
python -m adf_mcp.server
# or, after `pip install -e .`:
adf-mcp-serverサーバーは stdio を介して通信します。ターミナルで直接実行するとハングしているように見えますが、これは想定どおりです。MCP クライアント (VS Code 拡張機能、Claude Code、mcp dev など) が stdin/stdout 経由で接続するのを待っています。
MCP 対応拡張機能のサーバー設定には、次を指定してください:
{
"command": "python",
"args": ["-m", "adf_mcp.server"],
"cwd": "/absolute/path/to/adf-mcp-server"
}接続したら、health_check ツールを呼び出してください。Azure に一切触れることなく、{"status": "ok", ...} を返すはずです。
pip install -e ".[dev]" pytest-asyncio
pytest -vsrc/adf_mcp/ を参照してください。server.py (MCP トランスポート)、config.py (設定)、logging_config.py (構造化ログ) が含まれます。ドメインロジックと Azure 接続は、Step 3 以降に src/adf_mcp/domain/ 配下に追加されます。
src/adf_mcp/ を参照してください。server.py (MCP トランスポート)、config.py (設定)、logging_config.py (構造化ログ) が含まれます。ドメインロジックと Azure 接続は、Step 3 以降に src/adf_mcp/domain/ 配下に追加されます。
クライアントに "server disconnected" と表示される場合: まず python -m adf_mcp.server が単体で正常に実行されることを確認してください。起動時の例外が発生すると、クライアントが接続する前にプロセスが終了してしまうためです。
クライアントが応答を解析できない、または出力が文字化けする場合: MCP プロトコル以外の何か (例: 紛れ込んだ print()) が stdout に書き込まれています。このプロジェクトのすべてのログが stderr に出力されるのは、まさにこのためです。
Report that the MCP server is up and configuration loaded correctly.
Useful as a first call from any MCP client (VS Code, Claude Code) to confirm the server started, before any Azure-dependent tools exist.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It clearly indicates a read-only status check (no mutation implied) and adds a timing context ('before any Azure-dependent tools exist') that is useful. It doesn't explicitly state 'no side effects' or failure behavior, but the output schema likely covers those details. The added context is valuable and goes beyond the tool's name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: two short sentences. The first front-loads the core purpose, and the second provides usage context. No filler or repetition, every sentence earned its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with an output schema present, the description fully covers purpose and usage. It tells the agent exactly when to call it and why, and the output schema handles return details. Nothing critical is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is trivially fully described (empty object). Per the baseline rule for 0 params, a score of 4 is appropriate; the description needs no parameter information since none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Report') and a clear resource ('MCP server is up and configuration loaded correctly'). It is obviously distinct from the sibling Azure tools (factories, pipelines, triggers), leaving no ambiguity about what this tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs when to use: 'first call from any MCP client (VS Code, Claude Code) to confirm the server started, before any Azure-dependent tools exist.' This gives clear context and implicitly advises against using it after other tools, making its role in the workflow obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0health_checkScored across 1 tool
There is only one tool, so no agent could confuse it with another. The health_check tool has a clear and unique purpose as a readiness probe.
The single tool name is clean and uses standard snake_case; however, with only one tool, no meaningful set-wide naming convention is established.
A server named adf-mcp-server exposing only a health_check tool is far below the minimum viable tool surface. This is a single trivial tool, which matches the lowest end of the scale.
The server gives agents no way to interact with the Azure Data Factory domain at all. A health check alone is severely incomplete and leaves every meaningful user workflow unavailable.
Read-only MCP server for AIStatusDashboard status, incidents, metrics, and fallback recommendations.
MCP uptime, schema, auth, and SLA receipt monitoring.
Governed data discovery, exact queries, decisions, simulations, and runtime utilities over MCP.
Read-only MCP for identity resolution and write guardrails.
