Model Context Protocol (MCP): Context & History

Model Context Protocol emerged as a response to the repetitive effort required to connect large language models (LLMs) with external services. Early AI integrations relied on ad‑hoc HTTP calls and custom schemas, which made scaling difficult. By standardizing request and response formats, MCP provides a common language that lets hosts and servers exchange structured data without reinventing the wheel each time a new tool is added.

Implementation & Best Practices

Before writing any code, outline the core components you need: a Python environment (3.10+), the FastMCP library, a clear data model for tasks, and a set of CRUD tools. Decide how you will expose read‑only resources and design prompt templates that guide the model's behavior. With this roadmap you can move smoothly from setup to testing.

Setting Up the Environment

Install FastMCP via pip install fastmcp or uv add fastmcp. Create a virtual environment to keep dependencies isolated. Verify the installation by running python -c "import fastmcp; print(fastmcp.__version__)".

Defining Tools

Tools are Python functions decorated with @mcp.tool(). They perform actions such as adding, updating, or deleting tasks. Example:

@mcp.tool()
def add_task(title: str, description: str) -> dict:
    task = {"id": len(task_list)+1, "title": title, "description": description, "status": "pending", "created": datetime.utcnow().isoformat()}
    task_list.append(task)
    return task

Repeat similar patterns for update_task and delete_task. Ensure each tool returns a JSON‑serializable object so the client can parse it easily.

Creating Resources

Resources expose data without modification. Use @mcp.resource() to wrap a function that returns the current task list or a filtered view.

@mcp.resource()
def list_tasks() -> list:
    return task_list

Resources keep the model informed about the system state, enabling it to make decisions based on up‑to‑date information.

Writing Prompts

Prompts define the conversational context for the model. A good prompt mentions the relevant resources and suggests a format for the response.

prompt = """
You have access to the task list via the 'list_tasks' resource. Summarize pending tasks and suggest a priority order. Return a JSON object with 'summary' and 'priority_list'.
"""

Consistent prompts reduce ambiguity and improve the quality of the model's output.

Running and Testing the Server

Start the FastMCP server with uvicorn task_server:app --reload. Use the built‑in client to verify each tool and resource works as expected. Create a test_client.py that connects, calls add_task, and prints the result. Successful execution confirms the end‑to‑end flow.

For a deeper look at protocol implementation patterns, see this guide on subissue handling. If you need to scale beyond a single process, review the scalable orchestration framework for ideas on distributed deployment.

Key takeaways:

• Standardize communication with MCP to avoid repetitive integration work.
• Use FastMCP decorators to expose tools and resources cleanly.
• Design clear prompts to guide model behavior.
• Test each component before scaling.