SystemManager
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@SystemManagerget system info for CPU"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.

1. Core Architecture
MCP uses a structured three-tier model to separate the AI's reasoning from the technical execution of tools.
The Three Components:
Host: The primary application (e.g., Claude Desktop, Cursor, or a custom IDE) that the user interacts with.
Client: A component within the Host that manages the connection, security, and protocol negotiation with the server.
Server: A lightweight, specialized service that exposes specific data (Resources), logic (Tools), or context (Prompts) to the AI.


Related MCP server: Windows MCP Server
2. Server Implementation (Python)
The FastMCP SDK provides a high-level abstraction for building servers quickly.
Defining a Server and Tool:
Tools allow the LLM to perform actions, such as querying a database or interacting with a local API.
from mcp.server.fastmcp import FastMCP
# 1. Initialize the FastMCP server instance
mcp = FastMCP("SystemManager")
# 2. Define a tool using the @mcp.tool decorator
@mcp.tool()
def get_system_info(category: str) -> str:
\"\"\"Provides system-specific metadata.\"\"\"
return f"Data for {category}"
3. The Communication Layer: JSON-RPC
MCP relies on JSON-RPC 2.0 for all messaging. This ensures that every request from the client and every response from the server follows a strict, predictable format.

4. Transport Mechanisms
To move JSON-RPC messages between the Client and Server, MCP defines two primary "pipes" or transport layers:
A. Standard I/O (stdio)
Used primarily for local integrations where the server runs as a subprocess.
No Network Configuration: Ideal for local development and desktop apps.
Subprocess Lifecycle: The server starts when the client connects and terminates when the client exits.

B. Streamable HTTP (SSE)
Used for remote or networked servers.
Server-Sent Events (SSE): The server sends events to the client.
HTTP POST: The client sends commands back to the server.
Scalability: Allows the AI to connect to tools hosted in the cloud.

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
async def call_mcp_tool(tool_name: str, arguments: dict) -> str:
params = StdioServerParameters(
command=sys.executable,
args=["currency_server.py"],
)
async with stdio_client(params) as (reader, writer):
async with ClientSession(reader, writer) as session:
await session.initialize()
# Call the currency conversion tool
result = await session.call_tool(tool_name, arguments)
print("result : " ,result)
# Extract and print the text content of the server response
text_content = result.content[0].text
print("text_content : " , text_content)
# print(f"Conversion Result: {text_content}")
return text_content
# Run the "convert_currency" tool
asyncio.run(
call_mcp_tool("convert_currency",
{"amount": 250.0, "from_currency": "USD", "to_currency": "EUR"})
)Resources in MCP Servers
MCP and LLMs: Tools

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