Using and building MCP servers

About me

Python Cloud Advocate at Microsoft

Formerly: UC Berkeley, Coursera, Khan Academy, Google

Find me online at:

About you

Drop a message in the Teams chat with:

• Name & location
• How are you using GitHub Copilot - in VS Code, App, or CLI?
• What are you hoping to learn (or what have you learnt) at GitHub Copilot Day?
• Have you used any MCP servers before?
• Make up a new meaning for "MCP" (e.g. "More Coffee Please")

AI agents

You've probably used an AI agent...

Coding agents like GitHub Copilot are AI agents.
Chatbots like Copilot and Scout also have agentic loops.
You can also build your own agents in code (Python/C#/etc).

The agentic loop in action

Agents rely on LLM tool-calling

MCP: Model Context Protocol

MCP is the open standard that tells agent runtimes how to discover and connect to the tools they can call.

🔗 MCP specification: modelcontextprotocol.io

MCP architecture

MCP client/server request flow

MCP clients

Exercise time

Exercise 1:
Connect GitHub Copilot to a public MCP server
exercise1.md

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Python MCP frameworks

FastMCP server skeleton

from fastmcp import FastMCP

mcp = FastMCP("Expenses tracker")

# Define tools, prompts, and resources here...

if __name__ == "__main__":
    mcp.run(transport="streamable-http", host="0.0.0.0", port=8420)
					

🔗 Docs: gofastmcp.com

MCP transports: STDIO vs. HTTP

Learn more: gofastmcp.com/deployment/running-server

Tool with FastMCP

@mcp.tool
async def add_expense(
    date: Annotated[date, "Date of the expense in YYYY-MM-DD format"],
    amount: Annotated[float, "Positive numeric amount of the expense"],
    category: Annotated[Category, "Category label"],
    description: Annotated[str, "Human-readable description of the expense"],
    payment_method: Annotated[PaymentMethod, "Payment method used"],
) -> str:
    """Add a new expense to the expenses.csv file."""
    # Implementation ...

    return f"Added expense: ${amount} for {description} on {date_iso}"
					

🔗 Full example: expenses_tracker.py

Tool signature → JSON schema

@mcp.tool
async def add_expense(
    date: Annotated[date, "YYYY-MM-DD"],
    amount: Annotated[float, "Amount"],
    category: Category,
    description: str,
    payment_method: PaymentMethod,
) -> str:
    """Add a new expense."""
    ...

{
	"name": "add_expense",
	"description": "Add a new expense.",
	"inputSchema": {
		"type": "object",
		"properties": {
			"date": {
				"type": "string",
				"format": "date"
			},
			"amount": {"type": "number"},
			"category": {"type": "string"},
			"description": {"type": "string"},
			"payment_method": {"type": "string"}
		},
		"required": ["date", "amount", "category",
			"description", "payment_method"]
	}
}

Resource with FastMCP

@mcp.resource("resource://expenses")
async def get_expenses_data():
    csv_content = f"Expense data ({len(expenses_data)} entries):\n\n"
    for expense in expenses_data:
        csv_content += (
            f"Date: {expense['date']}, "
            f"Amount: ${expense['amount']}, "
            f"Category: {expense['category']}, "
            f"Description: {expense['description']}, "
            f"Payment: {expense['payment_method']}\n"
        )
    return csv_content
					

🔗 Full example: expenses_tracker.py

Prompt with FastMCP

@mcp.prompt
def analyze_spending_prompt(
    category: str | None = None,
    start_date: str | None = None,
    end_date: str | None = None,
) -> str:
    # Build filters ...
    return f"""Please analyze my spending patterns {filter_text} and provide:
    1. Total spending breakdown by category
    2. Average daily/weekly spending
    3. Most expensive single transaction
    4. Payment method distribution"""
					

🔗 Full example: expenses_tracker.py

Run the server

Start the server:

uv run servers/expenses_tracker.py

Point agent at the local URL:

http://localhost:8000/mcp

Test with MCP Inspector or MCPJam

npx @modelcontextprotocol/inspector

npx @mcpjam/inspector@latest

Useful debugging loop: direct server logs first, Inspector or MCPJam second, LLM client last.

Exercise time

Exercise 3:
Build your own product-store MCP server
exercise3.md

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MCP as a collaboration protocol

New MCP features turn agents from executors into collaborators.

Elicitations

Elicitations let servers request user input through the client.

Form mode elicitation flow

MCP Apps

MCP Apps let tools return rich, interactive UI that renders inside chat.

MCP Apps flow

Making MCP Apps with FastMCP

Four paths, from Python components to custom UI.

Learn more: gofastmcp.com/apps/overview

Exercise time

Exercise 4:
Add visual UI and purchase confirmation
exercise4.md

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Use more MCP servers

Connect your agents to more MCP servers to enhance your productivity:

• Azure DevOps MCP — query work items, repos, pipelines, and wikis in Azure DevOps
• Work IQ MCP — search emails, meetings, documents, and Teams messages from Microsoft 365
• Azure MCP — manage Azure resources, storage, and AI services

Build more MCP servers

If you are putting an MCP server in production, consider:

• Testing: Test with both direct MCP tools and LLM-driven clients.
• Evaluation: Optimize tool descriptions for user scenarios across agents.
• Usage controls: Rate-limit expensive or state-changing tools.
• Observability: Log tool calls without logging sensitive payloads.
• Security: Continuously audit tools for security risks and logs for abuse.

Security questions around MCP servers

Thank you!

Repository:
github.com/pamelafox/github-copilot-mcp-tutorial

Check out more of my MCP talks:

• Improving MCP tool schemas to increase agent reliability
• Identity-aware MCP Servers with Azure Cosmos DB
• An MCP for your Postgres DB
• Python + MCP 3-part series