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KNOWLEDGE_ASSISTANT_README.md

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# Personal Knowledge Assistant
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This guide demonstrates how to build an AI-powered personal assistant using Robyn's MCP (Model Context Protocol) implementation.
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## Overview
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The personal knowledge assistant connects AI assistants like Claude Desktop to your development environment, providing seamless access to:
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- File system operations (read, search, organize)
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- Task and note management
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- System monitoring and git integration
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- Web content fetching and analysis
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- Context-aware code analysis
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## Quick Start
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1. Run the MCP server:
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```bash
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python examples/agents.py
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```
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2. Connect your AI assistant to `http://localhost:8080/mcp`
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3. Start using natural language commands:
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- "What files are in my projects directory?"
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- "Show me my recent git commits"
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- "Create a note about today's standup meeting"
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- "What processes are using the most CPU?"
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- "Add a task to review the quarterly report"
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## Use Cases
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### File Management
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Access and organize files across your development workspace:
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- Browse directory structures
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- Search file contents
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- Read and analyze code files
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### Note Taking
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Create and manage markdown notes:
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- Meeting notes with timestamps
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- Project documentation
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- Code review findings
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### Task Management
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Track development tasks:
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- Add tasks with priorities and due dates
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- Mark tasks as complete
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- View task lists and status
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## Configuration
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The assistant creates the following structure:
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```
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~/Documents/
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├── notes/ # Markdown notes
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└── tasks.json # Task list
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~/projects/ # Development projects
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├── project1/
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└── project2/
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```
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## Security
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- File access restricted to home directory
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- Safe mathematical expression evaluation
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- Path validation for all file operations
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- Read-only git operations
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## Available Resources
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### File System
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- `fs://{path}` - Read files in home directory
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- `fs://dir/{path}` - List directory contents
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### Git Integration
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- `git://repo/{repo_name}` - Repository status and commits
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### System Monitoring
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- `system://processes` - Running processes
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- `system://stats` - System statistics
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## Available Tools
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- `create_note(title, content, tags)` - Create markdown notes
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- `add_task(task, priority, due_date)` - Add tasks
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- `complete_task(task_id)` - Mark tasks complete
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- `search_files(query, directory)` - Search file contents
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- `fetch_url_content(url, max_length)` - Download web content
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## Available Prompts
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- `analyze_file_structure(directory)` - Generate project analysis
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- `code_review_request(file_path, focus_area)` - Create code reviews
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- `task_prioritization(context)` - Organize and prioritize work
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## Dependencies
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Optional enhanced functionality:
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```bash
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pip install psutil # Enhanced system monitoring
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```
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## Implementation Examples
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### Development Workflow
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"Analyze my projects directory and help prioritize work based on recent activity"
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### Project Analysis
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"Review my web-app project structure and suggest improvements"
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### Meeting Notes
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"Create a note about today's architecture review with key decisions"
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### Code Search
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"Find all files mentioning 'authentication' and summarize approaches"
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### Task Management
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"Add high-priority task to refactor user service, due Friday"
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## Integration Benefits
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Connecting AI assistants to your development environment enables:
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- Native file system browsing
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- Context-aware project conversations
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- Personalized code suggestions
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- Real-time task management
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- Workspace-specific code reviews
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## Advanced Features
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The MCP implementation includes:
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- URI templates with parameter extraction
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- Auto-generated schemas from type hints
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- Async/sync operation handlers
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- MCP-compliant error handling
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- Type-safe parameter passing
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Extend easily with custom resources, tools, and prompts for your specific workflow.

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