Python MCP Agent Workshop

A workshop for building AI agents using the Model Context Protocol (MCP) in Python. This project walks through implementing MCP server fundamentals (JSON-RPC framing, tool discovery, and tool execution) and ships two fully working tools:

1. Performance Profiler - Semantic Python code analysis using Astroid AST parsing to detect performance anti-patterns (N+1 queries, blocking I/O in async, inefficient loops, memory issues)
2. Keyword Search - Async file system search with statistical analysis

Link to Presentation Slides

Presentation Slides

Features

Performance Profiler

The performance profiler uses Astroid for semantic AST analysis, detecting:

• N+1 Query Detection - Identifies Django and SQLAlchemy ORM queries inside loops
• Blocking I/O in Async - Finds synchronous operations (open, time.sleep, requests) in async functions
• Inefficient Loop Patterns - Detects string concatenation in loops, deep nesting
• Memory Inefficiency Detection - Flags loading entire files into memory (read(), json.load, pickle.load)

Keyword Search

• Asynchronous file system search across multiple directories
• Multi-format text file support
• Statistical analysis and reporting

Why Astroid over Standard AST?

Unlike general linters (Pylint, Flake8), the performance profiler uses Astroid for semantic analysis:

• Type Inference: Understands what types variables hold, not just their names
• Call Resolution: Knows which function is actually being called (e.g., Django ORM vs. regular method)
• Cross-module Analysis: Can resolve imports and understand relationships
• Pattern Context: Detects patterns like "ORM query inside a loop" that require understanding code structure

Quick Start

# Clone and install
git clone <repository-url>
cd python-mcp-agent-workshop
poetry install

# Verify setup
python verification.py

# Start the MCP server
poetry run workshop-mcp-server

# Run tests (102 tests)
poetry run pytest

# Use the performance profiler agent (requires Qodo)
qodo performance_analysis --set file_path="path/to/code.py"

# Use the keyword search agent (requires Qodo)
qodo keyword_analysis --set keyword="{KEYWORD_HERE}"

Testing the Performance Profiler via MCP

# Start the server and send a JSON-RPC request
echo '{"jsonrpc":"2.0","id":1,"method":"call_tool","params":{"name":"performance_check","arguments":{"file_path":"src/workshop_mcp/server.py"}}}' | poetry run python -m workshop_mcp.server

Learning Path (From-Scratch MCP Server)

Start with the protocol fundamentals and build up the server step by step:

1. 00 - Introduction
2. 01 - Transport: Framing MCP Messages Over stdio
3. 02 - JSON-RPC 2.0: Validating and Routing Requests
4. 03 - Initialize: Capability Handshake
5. 04 - Tools: Advertising and Invoking Capabilities

Prerequisites

Before starting the workshop, ensure you have the following installed:

Required Software

1. Python 3.11+

   • Download from python.org
   • Verify: python --version

2. Poetry (Python dependency management)

   • Install: curl -sSL https://install.python-poetry.org | python3 -
   • Or via pip: pip install poetry
   • Verify: poetry --version

Key Dependencies

The project uses these core libraries (installed via poetry install):

• Astroid - Advanced AST analysis for semantic Python parsing (powers performance profiler)
• aiofiles - Async file I/O operations

Optional Tools

• Git for version control
• VS Code or your preferred IDE
• Docker (for containerized deployment)
• Qodo Command (optional, for agent execution)
  • Install: npm install -g @qodo/command
  • Log in: qodo login
  • Verify: qodo --version
  • Docs: docs.qodo.ai/qodo-documentation/qodo-command

Architecture Overview

This workshop demonstrates a complete MCP ecosystem implemented from scratch:

┌─────────────────────────────────────────────────────────────────────┐
│                           AI Agents                                  │
│  ┌─────────────────────┐       ┌─────────────────────┐              │
│  │ performance_profiler│       │  keyword_analysis   │              │
│  │       .toml         │       │       .toml         │              │
│  └──────────┬──────────┘       └──────────┬──────────┘              │
└─────────────┼─────────────────────────────┼─────────────────────────┘
              │                             │
              ▼                             ▼
┌─────────────────────────────────────────────────────────────────────┐
│                        MCP Server (server.py)                        │
│                     JSON-RPC 2.0 over stdio                          │
├─────────────────────────────────────────────────────────────────────┤
│  Tools:                                                              │
│  ┌─────────────────────┐       ┌─────────────────────┐              │
│  │  performance_check  │       │   keyword_search    │              │
│  └──────────┬──────────┘       └──────────┬──────────┘              │
└─────────────┼─────────────────────────────┼─────────────────────────┘
              │                             │
              ▼                             ▼
┌─────────────────────────┐    ┌─────────────────────────┐
│  Performance Profiler   │    │    Keyword Search       │
│  ├─ AST Analyzer        │    │    (Async File I/O)     │
│  │  (Astroid-powered)   │    │                         │
│  ├─ Performance Checker │    │                         │
│  └─ Pattern Detection   │    │                         │
└─────────────────────────┘    └─────────────────────────┘

Components

1. MCP Server (src/workshop_mcp/server.py)

   • Implements MCP protocol over stdio (Content-Length framing)
   • Exposes performance_check and keyword_search tools
   • Handles JSON-RPC request routing and error handling

2. Performance Profiler (src/workshop_mcp/performance_profiler/)

   • ast_analyzer.py - Astroid-based semantic AST analysis
   • performance_checker.py - Issue detection and reporting
   • patterns.py - Anti-pattern definitions (ORM queries, blocking I/O, etc.)

3. Keyword Search Tool (src/workshop_mcp/keyword_search.py)

   • Asynchronous file system search
   • Multi-format text file support
   • Statistical analysis and reporting

4. AI Agents (agents/)

   • performance_profiler.toml - Performance analysis agent (Gemini 2.5 Pro)
   • keyword_analysis.toml - Keyword search agent

Usage Examples

Performance Profiler (Programmatic)

from workshop_mcp.performance_profiler import PerformanceChecker

# Analyze a file
checker = PerformanceChecker(file_path="path/to/code.py")
issues = checker.check_all()

for issue in issues:
    print(f"[{issue.severity.value}] {issue.category.value}")
    print(f"  Line {issue.line_number}: {issue.description}")
    print(f"  Suggestion: {issue.suggestion}")

# Get summary
summary = checker.get_summary()
print(f"Total issues: {summary['total_issues']}")
print(f"Critical: {summary['by_severity']['critical']}")

# Analyze source code directly
checker = PerformanceChecker(source_code="""
async def fetch_users():
    with open('data.json') as f:  # Blocking I/O in async!
        return json.load(f)
""")
issues = checker.check_all()

Basic Keyword Search

from workshop_mcp.keyword_search import KeywordSearchTool

tool = KeywordSearchTool()
result = await tool.execute("async", ["/path/to/codebase"])

print(f"Found {result['summary']['total_occurrences']} occurrences")
print(f"Most frequent file: {result['summary']['most_frequent_file']}")

Running the MCP Server

# Start server (listens on stdin/stdout)
poetry run workshop-mcp-server

# Or use the script entry point
poetry run python -m workshop_mcp.server

Agent Analysis

# Run performance analysis agent
qodo performance_analysis --set file_path="src/workshop_mcp/server.py"

# Run keyword analysis agent
qodo keyword_analysis --set keyword="{KEYWORD_HERE}"

Understanding Performance Profiler Output

The performance profiler returns issues with severity levels:

Severity	Description
CRITICAL	Blocking I/O in async functions - blocks entire event loop
HIGH	N+1 queries, memory inefficiencies - significant performance impact
MEDIUM	Inefficient loops, string concatenation - moderate impact
LOW	Minor optimizations, style suggestions

Example output structure:

{
  "summary": {
    "total_issues": 3,
    "by_severity": {"critical": 1, "high": 1, "medium": 1, "low": 0},
    "by_category": {"blocking_io_in_async": 1, "n_plus_one_query": 1, "inefficient_loop": 1}
  },
  "issues": [
    {
      "category": "blocking_io_in_async",
      "severity": "critical",
      "line_number": 15,
      "description": "Blocking I/O operation 'open' in async function",
      "suggestion": "Use aiofiles.open for async file operations",
      "function_name": "fetch_data"
    }
  ]
}

Development Setup

Environment Setup

1. Clone and Install

   git clone <repository-url>
   cd python-mcp-agent-workshop
   poetry install
   

2. Verify Installation

   python verification.py
   

3. Development Dependencies

   poetry install --with dev
   

Code Quality Tools

# Format code
poetry run black src/ tests/

# Sort imports
poetry run isort src/ tests/

# Type checking
poetry run mypy src/

Running in Development Mode

# Install in editable mode
poetry install

# Run server with debug logging
PYTHONPATH=src poetry run python -m workshop_mcp.server

# Run tests with verbose output
poetry run pytest -v -s

Project Structure

python-mcp-agent-workshop/
├── pyproject.toml              # Poetry configuration
├── README.md                   # This file
├── verification.py             # Setup verification script
├── agent.toml                  # Top-level agent configuration
├── mcp.json                    # MCP configuration metadata
│
├── src/workshop_mcp/           # Main package
│   ├── __init__.py             # Package initialization
│   ├── server.py               # MCP server implementation
│   ├── keyword_search.py       # Keyword search tool
│   └── performance_profiler/   # Performance analysis module
│       ├── __init__.py         # Module exports
│       ├── ast_analyzer.py     # Astroid-based AST analysis
│       ├── patterns.py         # Anti-pattern definitions
│       └── performance_checker.py  # Issue detection
│
├── agents/                     # Agent configurations
│   ├── keyword_analysis.toml   # Keyword search agent
│   └── performance_profiler.toml   # Performance analysis agent
│
├── tests/                      # Test suite (102 tests)
│   ├── test_keyword_search.py      # Keyword search tests (15)
│   ├── test_ast_analyzer.py        # AST analyzer tests (41)
│   ├── test_performance_checker.py # Performance checker tests (31)
│   ├── test_mcp_server_integration.py  # MCP integration tests (10)
│   ├── test_mcp_server_protocol.py # Protocol tests (5)
│   ├── test_agent_config.py        # Agent config validation
│   └── test_e2e_workflow.py        # End-to-end workflow tests

Testing Strategy

The project includes 102 tests covering:

Test Suites

• AST Analyzer (41 tests): Function extraction, loop detection, import analysis, call tracking
• Performance Checker (31 tests): N+1 queries, blocking I/O, inefficient loops, memory issues
• Keyword Search (15 tests): File search, edge cases, concurrency
• MCP Integration (10 tests): Tool registration, JSON-RPC handling, error responses
• Protocol Tests (5 tests): Message framing, content-length parsing
• E2E Workflow: Complete agent workflow validation
• Agent Config: TOML validation, output schema verification

Running Tests

# Run all tests
poetry run pytest

# Run with verbose output
poetry run pytest -v

# Run specific test file
poetry run pytest tests/test_performance_checker.py -v

# Run with coverage
poetry run pytest --cov=workshop_mcp

Troubleshooting

Common Issues

1. Python Version Error

   Error: Python 3.11+ required
   Solution: Upgrade Python or use pyenv
   

2. Poetry Not Found

   Error: poetry: command not found
   Solution: Install Poetry from python-poetry.org
   

3. Permission Denied

   Error: Permission denied accessing files
   Solution: Check file permissions and user access
   

4. Agent Configuration Error

   Error: Invalid TOML configuration
   Solution: Validate TOML syntax in agents/keyword_analysis.toml
   

Debug Mode

Enable detailed logging:

export LOG_LEVEL=DEBUG
poetry run workshop-mcp-server

Verification Script

Run the comprehensive verification (use python3.12 if it is installed; otherwise use your default python that meets the 3.11+ requirement):

python3.12 verification.py
# or
python verification.py

This checks:

• Python version compatibility
• Poetry installation and configuration
• Project structure completeness
• Dependency installation success
• MCP server functionality
• Unit test execution
• Agent configuration validity

Learning Resources

MCP Protocol

• MCP Specification
• MCP Examples

Python Async Programming

• AsyncIO Documentation
• Real Python Async Guide

Agent Development

• Qodo Documentation
• Agent Configuration Guide

Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Make changes and test: poetry run pytest
4. Commit changes: git commit -m 'Add amazing feature'
5. Push to branch: git push origin feature/amazing-feature
6. Open Pull Request

Development Guidelines

• Follow PEP 8 style guidelines
• Add type hints to all functions
• Write comprehensive docstrings
• Test-driven development: Write tests before implementing features
• Include unit tests for new features (maintain 102+ test count)
• Update documentation as needed

Adding New Performance Checks

1. Define the pattern in src/workshop_mcp/performance_profiler/patterns.py
2. Add detection logic in performance_checker.py
3. Write tests in tests/test_performance_checker.py
4. Update documentation

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Model Context Protocol team for the excellent protocol specification
• Poetry team for dependency management
• Qodo team for agent development platform
• Python community for async/await support

Support

• Issues: Use your fork's issue tracker
• Discussions: Use your fork's discussions board

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Happy coding!

This workshop provides a solid foundation for building production-ready MCP agents in Python. Extend and customize it for your specific use cases.