> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
> Use this file to discover all available pages before exploring further.

# Microsoft Learn: grounded answers

> Ground an agent in official Microsoft Learn documentation instead of its training data, using the free Learn MCP server.

Microsoft's official Learn MCP server provides tools to search and fetch current content from Microsoft Learn — Azure, .NET, C#, and the rest. Free, **no authentication**.

The reason to use it is not convenience. Cloud APIs move faster than model training runs, so an ungrounded answer about Azure authentication is a guess with a citation-shaped confidence. This server replaces the guess with the current page.

|                     |                                                 |
| ------------------- | ----------------------------------------------- |
| **Server**          | `https://learn.microsoft.com/api/mcp`           |
| **Auth**            | none                                            |
| **Tools**           | `microsoft_docs_search`, `microsoft_docs_fetch` |
| **Model used here** | `groq/llama-3.3-70b-versatile`                  |

## Prerequisites

* Python 3.10+
* A Groq API key (free tier available at [console.groq.com](https://console.groq.com)). Any tool-calling model works.

This tutorial deliberately uses an open-weight model on Groq to make a point: the value here comes from the retrieved documentation, not from the model's parametric knowledge. A smaller model with the right page in front of it beats a larger one working from memory.

## Build it

<Steps>
  <Step title="Install and set your key">
    ```bash theme={null}
    pip install -U swarms
    export GROQ_API_KEY="gsk_..."
    ```
  </Step>

  <Step title="Create the agent">
    ```python theme={null}
    from swarms import Agent

    agent = Agent(
        agent_name="MS-Learn-Agent",
        agent_description="Answers Microsoft/Azure/.NET questions from official Learn docs.",
        model_name="groq/llama-3.3-70b-versatile",
        mcp_url="https://learn.microsoft.com/api/mcp",
        max_loops=1,
    )
    ```
  </Step>

  <Step title="Ask a question whose answer has changed recently">
    ```python theme={null}
    result = agent.run(
        "Search Microsoft Learn and summarize how to authenticate a "
        "Python app to Azure using DefaultAzureCredential. Cite the docs."
    )
    print(result)
    ```

    Authentication guidance is a good test case precisely because it is the kind of thing models get confidently wrong from stale training data.
  </Step>

  <Step title="Verify it actually searched">
    Set `verbose=True` on the agent while you are developing. You will see the tool calls in the log — if there are none, the model answered from memory and the grounding did not happen.

    ```python theme={null}
    agent = Agent(
        agent_name="MS-Learn-Agent",
        model_name="groq/llama-3.3-70b-versatile",
        mcp_url="https://learn.microsoft.com/api/mcp",
        max_loops=1,
        verbose=True,
    )
    ```
  </Step>
</Steps>

## The complete script

```python theme={null}
from swarms import Agent

agent = Agent(
    agent_name="MS-Learn-Agent",
    agent_description="Answers Microsoft/Azure/.NET questions from official Learn docs.",
    model_name="groq/llama-3.3-70b-versatile",
    mcp_url="https://learn.microsoft.com/api/mcp",
    max_loops=1,
)

if __name__ == "__main__":
    result = agent.run(
        "Search Microsoft Learn and summarize how to authenticate a "
        "Python app to Azure using DefaultAzureCredential. Cite the docs."
    )
    print(result)
```

## Making grounding stick

Three prompt-level habits, in the order they pay off:

1. **Say what to do when the search fails.** "If the docs do not cover it, say so" prevents the most common failure — a plausible answer assembled from training data after an empty search.
2. **Ask for the URL, not just the claim.** A cited answer is checkable; an uncited one is not.
3. **Give it two loops.** Search results often need a follow-up fetch before the answer is complete.

```python theme={null}
SYSTEM_PROMPT = (
    "You answer Azure and .NET questions from official Microsoft Learn "
    "documentation. Always search before answering, and fetch the page when "
    "a search snippet is not enough. Quote the docs for any API surface you "
    "describe and give the URL. If Learn does not document what was asked, "
    "say so rather than answering from memory — this stack changes faster "
    "than training data."
)
```

<Note>
  Source: [examples/mcp/agents/03\_microsoft\_learn\_docs.py](https://github.com/kyegomez/swarms/blob/master/examples/mcp/agents/03_microsoft_learn_docs.py)
</Note>

## Next

* [Several servers at once](/examples/mcp/multi-server-agent) — cross-reference Learn with a repo server in one run.
* [Exa web search](/examples/mcp/exa-web-search) — when the answer is not in any one vendor's docs.