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., "@adf-mcp-server run a health check"
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.
Read-only MCP (Model Context Protocol) server for Azure Data Factory monitoring and root-cause analysis, built for use from VS Code / Claude Code.
Status: Step 1 (skeleton + health check). No Azure connectivity yet - that's added in Step 2 (auth) and Step 3 (ADF tools).
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.env currently only needs the server settings block (ADF_MCP_*). The
AZURE_* lines are placeholders for Step 2 - leave them commented out for now.
python -m adf_mcp.server
# or, after `pip install -e .`:
adf-mcp-serverThe server communicates over stdio - running it directly in a terminal
will look like it hangs; that's expected, it's waiting for an MCP client
(VS Code extension, Claude Code, mcp dev, etc.) to connect via stdin/stdout.
Point your MCP-capable extension's server config at:
{
"command": "python",
"args": ["-m", "adf_mcp.server"],
"cwd": "/absolute/path/to/adf-mcp-server"
}Once connected, call the health_check tool - it should return
{"status": "ok", ...} without touching Azure at all.
pip install -e ".[dev]" pytest-asyncio
pytest -vSee src/adf_mcp/ - server.py (MCP transport), config.py (settings),
logging_config.py (structured logging). Domain logic and Azure
connectivity are added under src/adf_mcp/domain/ from Step 3 onward.
Client shows "server disconnected" immediately: check python -m adf_mcp.server runs cleanly on its own first - a startup exception will
kill the process before the client ever connects.
Client can't parse responses / garbled output: something wrote to
stdout other than the MCP protocol itself (e.g. a stray print()). All
logging in this project goes to stderr for exactly this reason.
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.
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