6.1k

Agents

This guide demonstrates how to build AI-powered agents using Robyn's MCP (Model Context Protocol) implementation.

Overview

The agent system connects AI assistants like Claude Desktop to your development environment, providing seamless access to:

  • File system operations (read, search, organize)
  • Task and note management
  • System monitoring and git integration
  • Web content fetching and analysis
  • Context-aware code analysis

Quick Start

  1. Run the MCP server:

    python examples/agents.py

  2. Connect your AI assistant to http://localhost:8080/mcp

  3. Start using natural language commands:

    • "What files are in my projects directory?"
    • "Show me my recent git commits"
    • "Create a note about today's standup meeting"
    • "What processes are using the most CPU?"
    • "Add a task to review the quarterly report"

Configuration

The assistant creates the following structure:

~/Documents/
├── notes/           # Markdown notes
└── tasks.json      # Task list

~/projects/          # Development projects
├── project1/
└── project2/

Security

  • File access restricted to home directory
  • Safe mathematical expression evaluation
  • Path validation for all file operations
  • Read-only git operations

Available Resources

File System

  • fs://{path} - Read files in home directory
  • fs://dir/{path} - List directory contents

Git Integration

  • git://repo/{repo_name} - Repository status and commits

System Monitoring

  • system://processes - Running processes
  • system://stats - System statistics

Available Tools

  • create_note(title, content, tags) - Create markdown notes
  • add_task(task, priority, due_date) - Add tasks
  • complete_task(task_id) - Mark tasks complete
  • search_files(query, directory) - Search file contents
  • fetch_url_content(url, max_length) - Download web content

Available Prompts

  • analyze_file_structure(directory) - Generate project analysis
  • code_review_request(file_path, focus_area) - Create code reviews
  • task_prioritization(context) - Organize and prioritize work

Dependencies

Optional enhanced functionality:

pip install psutil  # Enhanced system monitoring

Implementation Examples

Development Workflow

"Analyze my projects directory and help prioritize work based on recent activity"

Project Analysis

"Review my web-app project structure and suggest improvements"

Meeting Notes

"Create a note about today's architecture review with key decisions"

Code Search

"Find all files mentioning 'authentication' and summarize approaches"

Task Management

"Add high-priority task to refactor user service, due Friday"

Integration Benefits

Connecting AI assistants to your development environment enables:

  • Native file system browsing
  • Context-aware project conversations
  • Personalized code suggestions
  • Real-time task management
  • Workspace-specific code reviews

Advanced Features

The MCP implementation includes:

  • URI templates with parameter extraction
  • Auto-generated schemas from type hints
  • Async/sync operation handlers
  • MCP-compliant error handling
  • Type-safe parameter passing

Extend easily with custom resources, tools, and prompts for your specific workflow.