MCP and LLMs: Prompts and Resources

Introduction to Model Context Protocol (MCP)

James Chapman

AI Curriculum Manager, DataCamp

Resources and Prompts in the LLM Flow

Resources: read-only context
Prompts: instruction templates to set the behavior and optimize the model for the task

→ Continue with the OpenAI Responses API

The Prompt-Resource Workflow

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Timezone Converter")

@mcp.tool()
def convert_timezone(date_time: str, from_timezone: str, to_timezone: str) -> str:
    # ...

@mcp.resource("file://locations.txt")
def get_locations() -> str:
    # ...

@mcp.prompt(title="Timezone Conversion")
def convert_timezone_prompt(timezone_request: str) -> str:
    # ...

if __name__ == "__main__":
    mcp.run(transport="stdio")

Client Helper Functions

read_resource(resource_uri): fetches a resource's contents by URI
read_prompt(prompt_name, user_input): fetches the prompt template with the user's request injected

1. Fetch Resource and Prompt

async def get_context_from_mcp(user_query: str) -> tuple[str, str]:
    """Fetch resource content and prompt text from the MCP server."""
    params = StdioServerParameters(command=sys.executable, args=["timezone_server.py"])

    async with stdio_client(params) as (reader, writer):
        async with ClientSession(reader, writer) as session:
            await session.initialize()

            # Get the resource (supported locations)
            resource_result = await session.read_resource("file://locations.txt")
            resource_text = resource_result.contents[0].text

            # Get the prompt with the user's query
            prompt_result = await session.get_prompt("convert_timezone_prompt",
                                                     arguments={"timezone_request": user_query})
            prompt_text = prompt_result.messages[0].content.text

            return resource_text, prompt_text

2. Build the System Message and Call the LLM

async def call_llm_with_context(user_query: str):
    """Call the LLM with resource and prompt context from MCP."""

    resource_text, prompt_text = await get_context_from_mcp(user_query)

    # Combine prompt (task + rules + user request) with resource (supported locations)
    full_prompt = prompt_text + "\n\nSupported locations:\n" + resource_text

    client = AsyncOpenAI(api_key="<OPENAI_API_TOKEN>")
    response = await client.responses.create(
        model="gpt-4o-mini",
        input=full_prompt,
        tools=openai_tools,  # from get_tools_from_mcp(), formatted for OpenAI
    )

3. Handling the Response

    output = response.output[0]

    if output.type == "message":
        print(f"\nAssistant: {output.content[0].text}")
        return str(output.content[0].text)


    if output.type == "function_call":
        args = json.loads(output.arguments)
        result = await call_mcp_tool(output.name, args)
        followup = await client.responses.create(
            model="gpt-4o-mini",
            input=[
                {"role": "user", "content": user_query},
                output,
                {"type": "function_call_output", "call_id": output.call_id, "output": result}
            ],
        )
        # ... then print the assistant's reply from followup.output

Example: Ambiguous Request

if __name__ == "__main__":
    asyncio.run(call_llm_with_context("What time is it in Canada?"))

Assistant: Canada has several time zones. Which city or region do you mean?
For example, Toronto, Vancouver, or Halifax?

Example: Clear Request

if __name__ == "__main__":
    asyncio.run(call_llm_with_context("It is 9:50 AM in the UK in January. What
        time is it in Lisbon, Portugal?"))

Assistant: It's 9:50 AM in Lisbon as well.

Recap: Resources and Prompts with the LLM

Let's practice!

Introduction to Model Context Protocol (MCP)