DAZ Command MCP Server

DAZ Command MCP Server

A Model Context Protocol (MCP) server that provides session-based command execution with intelligent LLM-powered summarization.

🚀 Features

• 🔧 Session Management: Create, open, and manage isolated command execution sessions
• ⚡ Command Execution: Run shell commands with timeout controls and working directory management
• 📁 File Operations: Read and write text files with comprehensive error handling
• 🤖 LLM Summarization: Automatic session progress tracking using structured LLM responses
• 📋 Event Logging: Complete audit trail of all operations within sessions
• 🔒 Thread-Safe: Robust concurrent operation with proper synchronization

📦 Installation

Prerequisites

• Python 3.8+
• fastmcp library
• dazllm library for LLM integration

Quick Setup

1. Clone this repository:

git clone https://github.com/yourusername/daz-command-mcp.git
cd daz-command-mcp

2. Install dependencies:

pip install -r requirements.txt

3. Configure your LLM model in the script (default: lm-studio:openai/gpt-oss-20b)

🎯 Usage

Starting the Server

python main.py

Available Tools

Session Management

• daz_sessions_list() - List all sessions and identify the active one
• daz_session_create(name, description) - Create and activate a new session
• daz_session_open(session_id) - Open and activate an existing session
• daz_session_current() - Get details of the currently active session
• daz_session_close() - Close the current session
• daz_session_rename(old_name, new_name) - Rename an existing session
• daz_session_delete(session_name) - Delete a session by moving to deleted_sessions

Command & File Operations

All command and file operations require an active session and context parameters:

• daz_command_cd(directory, current_task, summary_of_what_we_just_did, summary_of_what_we_about_to_do) - Change working directory
• daz_command_read(file_path, current_task, summary_of_what_we_just_did, summary_of_what_we_about_to_do) - Read a text file
• daz_command_write(file_path, content, current_task, summary_of_what_we_just_did, summary_of_what_we_about_to_do) - Write a text file
• daz_command_run(command, current_task, summary_of_what_we_just_did, summary_of_what_we_about_to_do, timeout=60) - Execute shell commands

Learning & Instructions

• daz_add_learnings(learning_info) - Add important discoveries and context to the session
• daz_instructions_read() - Read current session instructions
• daz_instructions_add(instruction) - Add a new instruction to the session
• daz_instructions_replace(instructions) - Replace all instructions with a new list
• daz_record_user_request(user_request) - Record a user request at the start of multi-step tasks

Example Workflow

# Create a new session
daz_session_create("Setup Project", "Setting up a new Python project with dependencies")

# Navigate to project directory  
daz_command_cd("/path/to/project", 
               "Setting up Python project",
               "Created new session for project setup", 
               "Navigate to project root directory")

# Run commands
daz_command_run("pip install -r requirements.txt",
                "Setting up Python project", 
                "Navigated to project directory",
                "Install project dependencies")

# Read configuration
daz_command_read("config.json",
                 "Setting up Python project",
                 "Installed dependencies successfully", 
                 "Review current configuration settings")

# Write new file
daz_command_write("setup.py", "...",
                  "Setting up Python project",
                  "Reviewed configuration file",
                  "Create package setup file")

🏗️ Architecture

Session Storage

Sessions are stored as JSON files in the sessions/ directory with the following structure:

{
  "id": "unique-session-id",
  "name": "Session Name", 
  "description": "Detailed description",
  "created_at": 1692123456.789,
  "updated_at": 1692123456.789,
  "summary": "LLM-generated summary",
  "progress": "Current progress status",
  "current_directory": "/current/working/dir",
  "events_count": 42
}

Event Logging

Every operation is logged with comprehensive details in event_log.jsonl:

{
  "timestamp": 1692123456.789,
  "type": "run|read|write|cd|user_request|learning",
  "current_task": "The task being worked on",
  "summary_of_what_we_just_did": "What was just completed",
  "summary_of_what_we_about_to_do": "What's planned next",
  "inputs": {...},
  "outputs": {...}, 
  "duration": 0.123
}

LLM Integration

The server uses asynchronous LLM processing to maintain session summaries:

• 🔄 Background Processing: Summarization runs in a sep