Click on "Install 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., "@MCP Demo Servercalculate 15% of 200"
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.
MCP Demo Server with Intelligent Agent System
A production-ready demonstration of a Model Context Protocol (MCP) server implemented in Python, featuring an intelligent Agent System with subagents and orchestration capabilities. This project showcases best practices for building MCP servers and multi-agent systems with comprehensive examples.
What is MCP?
The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). MCP servers expose:
Tools: Executable functions that LLMs can call (e.g., calculator, file operations)
Resources: Data sources that LLMs can read (e.g., configuration, documentation)
Prompts: Reusable prompt templates for common tasks
Features
Agent System (NEW!)
Production-ready intelligent agents that work with MCP:
Agent - Core agent with MCP integration
Execute tasks using MCP tools
Delegate work to specialized subagents
Track task execution and results
Handle errors, retries, and timeouts
SubAgents - Specialized agents for specific tasks
CalculatorSubAgent - Mathematical calculations
FileOperationsSubAgent - File system operations
WeatherSubAgent - Weather information
TimestampSubAgent - Timestamp operations
DataProcessingSubAgent - Data processing pipelines
AgentOrchestrator - Multi-agent coordination
Manage pools of agents
Auto-assign tasks to capable agents
Execute workflows with dependencies
Parallel and sequential execution
Result aggregation and statistics
Task Management - Robust task system
Task definitions with priorities
State tracking (pending, in_progress, completed, failed)
Retry logic and timeout handling
Detailed result tracking
MCP Server Tools
Calculator - Basic mathematical operations
Operations: add, subtract, multiply, divide
Full input validation and error handling
File Operations - File system interactions
Read, write, list directories, check file existence
Safe path handling with proper error messages
Weather - Simulated weather information
Get weather data for any city
Support for Celsius and Fahrenheit
Timestamp - Get current time in various formats
ISO format, Unix timestamp, human-readable format
Resources Available
Server Configuration (
config://server/settings)Current server settings and metadata
JSON formatted configuration
System Information (
system://info)OS, Python version, working directory
Server process information
Documentation (
docs://getting-started)Getting started guide
Usage instructions
Prompts Provided
Code Review - Generate code review checklists
Customizable by programming language
Adjustable complexity level
Documentation - Documentation templates
API, User, and Developer documentation types
Project-specific customization
Debug Assistant - Debugging guidance
Structured debugging approach
Common techniques and best practices
Installation
Prerequisites
Python 3.10 or higher
pip (Python package manager)
Install Dependencies
# Clone the repository
git clone https://github.com/yourusername/mcp-agent.git
cd mcp-agent
# Install the package
pip install -e .
# Or install dependencies directly
pip install -r requirements.txtDevelopment Setup
# Install development dependencies
pip install -r requirements-dev.txt
# Run tests
pytest
# Format code
black src/
# Lint code
ruff check src/Usage
Running the Agent System Demo
Experience the full power of the agent system:
# Run comprehensive agent system demo
python examples/agent_demo.pyThis demo showcases:
Simple agent with basic tasks
Agent with subagents (task delegation)
Orchestrator managing multiple agents
Multi-step workflow execution
Data processing pipeline
Quick Agent System Example
import asyncio
from mcp_demo import Agent, AgentCapability, Task
from mcp_demo import CalculatorSubAgent, FileOperationsSubAgent
from mcp_demo import AgentOrchestrator, Workflow, WorkflowStep
async def main():
# Create main agent with subagents
main_agent = Agent(name="MainAgent")
calc_sub = CalculatorSubAgent(parent_agent=main_agent)
file_sub = FileOperationsSubAgent(parent_agent=main_agent)
# Create tasks
calc_task = Task(
name="Calculate",
task_type="calculation",
parameters={"operation": "add", "a": 10, "b": 20},
)
# Execute - automatically delegated to appropriate subagent
async with main_agent, calc_sub, file_sub:
result = await main_agent.execute_task(calc_task)
print(f"Result: {result.data}")
asyncio.run(main())Using the Orchestrator
from mcp_demo import AgentOrchestrator, CalculatorSubAgent, Task
async def orchestrate():
# Create orchestrator
orch = AgentOrchestrator(name="MainOrch")
# Register agents
orch.register_agent(CalculatorSubAgent())
# Execute tasks in parallel
async with orch:
results = await orch.execute_tasks(
[task1, task2, task3],
parallel=True,
)
# Get statistics
stats = orch.get_statistics()
print(f"Success rate: {stats['overall_success_rate']:.1f}%")Running the MCP Server
The server uses stdio for communication, which is the standard transport for MCP servers:
# Run directly with Python
python -m mcp_demo.server
# Or use the installed script
mcp-demoConfiguration for Claude Desktop
To use this MCP server with Claude Desktop, add it to your Claude configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"demo": {
"command": "python",
"args": [
"-m",
"mcp_demo.server"
],
"env": {}
}
}
}Or using the installed command:
{
"mcpServers": {
"demo": {
"command": "mcp-demo",
"args": [],
"env": {}
}
}
}Using with MCP Client
You can also use the included example client:
python examples/client.pyProject Structure
mcp-agent/
├── src/
│ └── mcp_demo/
│ ├── __init__.py # Package initialization
│ ├── server.py # MCP server implementation
│ ├── agent.py # Core agent with MCP integration
│ ├── subagent.py # Specialized subagents
│ ├── orchestrator.py # Multi-agent orchestration
│ └── task.py # Task management system
├── examples/
│ ├── client.py # Example MCP client
│ ├── agent_demo.py # Comprehensive agent system demo
│ └── claude_config.json # Example Claude Desktop config
├── tests/
│ ├── __init__.py
│ ├── test_server.py # Server tests
│ └── test_agents.py # Agent system tests
├── docs/
│ ├── ARCHITECTURE.md # MCP server architecture
│ └── AGENT_SYSTEM.md # Agent system documentation
├── pyproject.toml # Project configuration
├── requirements.txt # Production dependencies
├── requirements-dev.txt # Development dependencies
├── README.md # This file
└── LICENSE # MIT LicenseCode Architecture
Server Implementation
The server follows a clean, modular architecture:
class MCPDemoServer:
"""Main server class with handler methods"""
def __init__(self, name: str):
"""Initialize server and register handlers"""
async def list_tools(self) -> list[Tool]:
"""Return available tools"""
async def call_tool(self, name: str, arguments: Any) -> list[TextContent]:
"""Execute a tool"""
async def list_resources(self) -> list[Resource]:
"""Return available resources"""
async def read_resource(self, uri: AnyUrl) -> str:
"""Read a resource"""
async def list_prompts(self) -> list[Prompt]:
"""Return available prompts"""
async def get_prompt(self, name: str, arguments: dict) -> GetPromptResult:
"""Get a prompt with arguments"""Key Design Patterns
Type Safety: Full type hints with Pydantic models
Error Handling: Comprehensive try-catch with logging
Validation: Input validation using Pydantic schemas
Logging: Structured logging to file and stderr
Async/Await: Proper async patterns throughout
Separation of Concerns: Each tool in its own method
Examples
Example 1: Using the Calculator Tool
# Tool call from LLM client
{
"name": "calculator",
"arguments": {
"operation": "add",
"a": 15,
"b": 27
}
}
# Response
{
"content": [
{
"type": "text",
"text": "Result: 15 add 27 = 42"
}
]
}Example 2: Reading a Resource
# Read server configuration
{
"uri": "config://server/settings"
}
# Response (JSON)
{
"server_name": "mcp-demo-server",
"version": "1.0.0",
"max_connections": 100,
"features": ["tools", "resources", "prompts"]
}Example 3: Getting a Prompt
# Get code review prompt
{
"name": "code-review",
"arguments": {
"language": "Python",
"complexity": "complex"
}
}
# Response: Detailed code review checklist for PythonDevelopment
Adding a New Tool
Define input schema with Pydantic:
class MyToolInput(BaseModel):
param1: str = Field(description="Description")
param2: int = Field(default=0, description="Description")Add tool to
list_tools():
Tool(
name="my_tool",
description="What this tool does",
inputSchema=MyToolInput.model_json_schema(),
)Implement tool logic:
async def _my_tool(self, arguments: dict) -> list[TextContent]:
tool_input = MyToolInput(**arguments)
# Your implementation here
return [TextContent(type="text", text="Result")]Add to
call_tool()dispatcher:
if name == "my_tool":
return await self._my_tool(arguments)Adding a New Resource
Add to
list_resources():
Resource(
uri=AnyUrl("my://resource"),
name="My Resource",
description="What this resource provides",
mimeType="application/json",
)Add handler in
read_resource():
if uri_str == "my://resource":
data = {"key": "value"}
return json.dumps(data, indent=2)Adding a New Prompt
Add to
list_prompts():
Prompt(
name="my-prompt",
description="What this prompt does",
arguments=[
{
"name": "arg1",
"description": "Argument description",
"required": True,
}
],
)Add handler in
get_prompt():
if name == "my-prompt":
arg1 = arguments.get("arg1")
message = f"Prompt template with {arg1}"
return GetPromptResult(
messages=[
PromptMessage(
role="user",
content=TextContent(type="text", text=message),
)
]
)Testing
Run the comprehensive test suite:
# Run all tests
pytest
# Run with coverage
pytest --cov=mcp_demo --cov-report=html
# Run specific test files
pytest tests/test_server.py # MCP server tests
pytest tests/test_agents.py # Agent system tests
# Run with verbose output
pytest -v
# Run agent tests only
pytest tests/test_agents.py -vBest Practices Demonstrated
Input Validation: All tool inputs validated with Pydantic
Error Handling: Comprehensive error handling with meaningful messages
Logging: Structured logging for debugging and monitoring
Type Safety: Full type hints throughout the codebase
Documentation: Comprehensive docstrings and comments
Testing: Unit tests for all major functionality
Code Quality: Formatted with Black, linted with Ruff
Async Patterns: Proper use of async/await
Resource Management: Proper cleanup and resource handling
Security: Safe file operations with path validation
Troubleshooting
Common Issues
Issue: Module not found error
# Solution: Install in development mode
pip install -e .Issue: Server not appearing in Claude Desktop
# Solution: Check configuration file path and JSON syntax
# Restart Claude Desktop after configuration changesIssue: Import errors for mcp package
# Solution: Install latest MCP SDK
pip install --upgrade mcpDebug Mode
Enable debug logging:
import logging
logging.basicConfig(level=logging.DEBUG)Check the log file:
tail -f mcp_server.logContributing
Contributions are welcome! Please:
Fork the repository
Create a feature branch (
git checkout -b feature/amazing-feature)Commit your changes (
git commit -m 'Add amazing feature')Push to the branch (
git push origin feature/amazing-feature)Open a Pull Request
Documentation
Agent System Documentation - Complete guide to the agent system
MCP Server Architecture - MCP server design and implementation
Agent Demo Examples - Comprehensive usage examples
Resources
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
Built with the MCP Python SDK
Inspired by the MCP community examples
Thanks to Anthropic for developing the Model Context Protocol
Support
Create an issue: GitHub Issues
Documentation: README
MCP Community: MCP Discord
Happy MCP Server Building! 🚀
This server cannot be installed
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.