Microsoft Learn: grounded answers

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). 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

Install and set your key

pip install -U swarms
export GROQ_API_KEY="gsk_..."

Create the agent

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,
)

Ask a question whose answer has changed recently

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.

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.

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,
)

The complete script

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:

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.
Ask for the URL, not just the claim. A cited answer is checkable; an uncited one is not.
Give it two loops. Search results often need a follow-up fetch before the answer is complete.

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."
)

Source: examples/mcp/agents/03_microsoft_learn_docs.py

Several servers at once — cross-reference Learn with a repo server in one run.
Exa web search — when the answer is not in any one vendor’s docs.

Index

Basic Examples

Model Providers

Single Agent

Multi-Agent Examples

Auto Agent Builder

Scheduled Agents

Applications

Research

Technical Analysis

Use Cases

Finance

Voice Agents

Integrations

MCP Tool Integrations

Deployment

CLI

Microsoft Learn: grounded answers

Prerequisites

Build it

The complete script

Making grounding stick

Next

​Prerequisites

​Build it

​The complete script

​Making grounding stick

​Next

Prerequisites

Build it

The complete script

Making grounding stick

Next