paelladoc 0.3.1

AI-First Development Framework - An MCP Implementation

🧠 PAELLADOC: The AI-First Development Framework

Version 0.3.0: This release marks a significant step, focusing PAELLADOC as an implementation of Anthropic's Model Context Protocol (MCP), enabling powerful AI-First development workflows through LLM interaction.

"In the AI era, context isn't supplementary to code—it's the primary creation."

PAELLADOC is an AI-First Development framework that implements the 5 Philosophical Principles of AI-First Development, transforming how we create software in the age of AI.

🎯 PAELLADOC and the Model Context Protocol (MCP)

PAELLADOC implements Anthropic's Model Context Protocol (MCP) (see Anthropic's news). This protocol provides a structured way for Large Language Models (LLMs) to interact with external tools and context, enabling more sophisticated capabilities.

By implementing MCP, PAELLADOC allows LLMs to leverage its specific AI-First development tools and workflows directly through this standard. This approach facilitates functionalities similar to Tool Use or Function Calling seen in other platforms, but specifically utilizes the Anthropic MCP standard for interaction.

🎯 The AI-First Philosophy

Traditional development treats documentation as an afterthought. AI-First Development inverts this paradigm:

• Context becomes the primary artifact
• Code becomes its manifestation
• Knowledge evolves alongside systems
• Decisions preserve their philosophical context
• Human-AI collaboration is seamless

🧠 The Five Principles in Action

1. Context as Primary Creation

# Traditional Way
write_code() -> document()

# PAELLADOC Way
create_context() -> manifest_as_code()

• Every artifact has a UUID for perfect traceability
• Context is versioned alongside code
• Knowledge graphs capture relationships
• Intent is preserved at every step

2. Intent-Driven Architecture

graph TD
    A[Business Intent] --> B[Context Creation]
    B --> C[Architecture Manifestation]
    C --> D[Code Generation]
    D --> E[Living Documentation]

• Architecture flows from intent, not implementation
• Every decision captures its philosophical context
• Systems adapt to evolving purpose

3. Knowledge as Living Entity

# Knowledge evolves with your system
paella continue my-project

• Project memory tracks evolution of understanding
• Documentation updates automatically with changes
• Context remains fresh and relevant
• Knowledge graphs show relationships

4. Human-AI Collaborative Consciousness

# Not just code generation, but true collaboration
with paelladoc.context() as ctx:
    ctx.understand_intent()
    ctx.propose_solutions()
    ctx.implement_with_human()

• Natural language conversations
• Intent preservation
• Contextual awareness
• Seamless collaboration

5. Contextual Decision Architecture

decision:
  id: uuid-123
  intent: "Why we chose this path"
  context: "What we knew at the time"
  alternatives: "What we considered"
  implications: "Future impact"

• Every decision preserves its context
• Future developers understand the "why"
• Changes respect historical context
• Intent remains clear

🚀 Installation & Integration

PAELLADOC is a Python application and should be installed in its own dedicated Python virtual environment. This keeps its dependencies separate and avoids conflicts. You'll need one PAELLADOC environment, regardless of how many different projects (Python, JS, Ruby, etc.) you plan to document.

(Requires Python 3.12 or later)

1. Create and Activate the Dedicated Environment

First, choose a permanent location for this environment. Your home directory is often a good choice.

# Navigate to where you want to store the environment (e.g., your home directory)
# cd ~  # Uncomment and run if you want it in your home directory

# Create the virtual environment (using python3.12 or your installed 3.12+ version)
# We'll name the folder '.paelladoc_venv' (starting with a dot makes it hidden)
python3.12 -m venv .paelladoc_venv

# Activate the environment 
# (The command depends on your shell. Use ONE of the following)

# For Bash/Zsh:
source .paelladoc_venv/bin/activate

# For Fish:
# source .paelladoc_venv/bin/activate.fish

# For Powershell (Windows):
# .\.paelladoc_venv\Scripts\activate.ps1 

(You should see (.paelladoc_venv) at the beginning of your terminal prompt now)

2. Install PAELLADOC in the Activated Environment

# Make sure your (.paelladoc_venv) prompt is visible before running pip
pip install paelladoc

3. Configure Your LLM (MCP Setup)

Now, tell your LLM tool (like Cursor) how to find and run the PAELLADOC you just installed inside its dedicated environment. This involves editing the tool's MCP JSON configuration file.

Key Information Needed:

• The Full Path to the Python Executable: You need the absolute path to the python file inside the .paelladoc_venv/bin (or Scripts on Windows) directory you created.
  • If you created it in your home directory (~), the path will likely be /Users/your_username/.paelladoc_venv/bin/python on macOS/Linux or C:\\Users\\your_username\\.paelladoc_venv\\Scripts\\python.exe on Windows. Replace your_username accordingly!
  • Tip: While the venv is active, you can often find the path by running which python (macOS/Linux) or where python (Windows).
• Database Location (Optional): By default, PAELLADOC stores its memory database in ~/.paelladoc/memory.db. You can override this using the PAELLADOC_DB_PATH environment variable in the MCP configuration if needed.

Cursor IDE Example

# Edit your .cursor/mcp.json file:
{
  "mcpServers": {
    "paelladoc": {
      "command": "/Users/your_username/.paelladoc_venv/bin/python", 
      "args": [
        "-m",
        "paelladoc.ports.input.mcp_server_adapter",
        "--stdio"
      ],
      "env": {
      }
    }
    // ... other servers
  }
}

Other LLMs (Claude, Copilot, etc.)

Configure the tool use settings similarly, always ensuring the command points to the full path of the Python executable inside your dedicated .paelladoc_venv. The exact JSON structure might vary slightly between platforms.

// Example structure (adapt as needed):
{
  // ... platform specific tool definition ...
  "command": "/Users/your_username/.paelladoc_venv/bin/python",
  "args": [ "-m", "paelladoc.ports.input.mcp_server_adapter", "--stdio" ],
  "env": {
  }
  // ...
}

4. Let the LLM Guide You

Once connected, your LLM will have access to all PAELLADOC commands:

• PAELLA: Start new documentation projects
• CONTINUE: Continue existing documentation
• VERIFY: Verify documentation coverage
• GENERATE: Generate documentation or code

The LLM will handle all the complexity - you just need to express your intent in natural language!

🚦 Version Stability

• PyPI Version (Stable): The versions published on PyPI (pip install paelladoc) are stable releases recommended for general use.
• GitHub Repository (Development): The main branch (and other branches) on the GitHub repository contains the latest development code. This version may include new features or changes that are not yet fully tested and should be considered unstable. Use this version if you want to try out cutting-edge features or contribute to development.

🚀 Quick Start

1. Ensure PAELLADOC is installed (pip install paelladoc) and configured in your LLM's tool/MCP settings (see examples above).

2. Start interacting with PAELLADOC through your LLM by issuing a command. The primary command to initiate a new project or list existing ones is PAELLA.

   • In Cursor or a similar chat interface, simply type:

     PAELLA
     

   • Alternatively, you can instruct the LLM more explicitly:

     Use PAELLADOC to start documenting a new project.
     

     Tell PAELLADOC I want to create documentation.
     

3. Follow the LLM's lead: PAELLADOC (via the LLM) will then guide you through the process interactively, asking for project details, template choices, etc.

⚙️ Available Commands

Once PAELLADOC is configured in your LLM (like Cursor) via MCP, you can interact with it using the following commands. The LLM will typically guide you through the necessary arguments interactively.

PAELLA

Initiates the documentation process for a new project or lists/selects existing ones.

• Arguments (guided by LLM):
  • project_name (string, optional): Name of the project to document.
  • project_type (string): Type of project (frontend, backend, chrome_extension, fullstack, mobile_app).
  • methodologies (comma-separated): Development methodologies (tdd, github_workflow).
  • git_workflow (string): Git workflow style (github_flow, gitflow, trunk_based, no_workflow).
  • generate_rules (boolean): Whether to generate Cursor rules from documentation.
  • ai_mode (string): AI operation mode (autonomous, collaborative, advisory).
  • -help (flag): Display help information.

HELP

Displays help information about available PAELLADOC commands.

• Arguments (guided by LLM):
  • command (string, optional): Specific command to get help for.
  • format (string): Output format (detailed, summary, examples).

CONTINUE

Continues working on an existing project's documentation.

• Arguments (guided by LLM):
  • project_name (string, required): Name of the project to continue with.
  • update_rules (boolean): Whether to update Cursor rules from documentation.
  • sync_templates (boolean): Whether to synchronize templates with current state.

ACHIEVEMENT

Records a significant achievement in the project memory.

• Arguments (guided by LLM):
  • description (string, required): Description of the achievement.
  • category (string, required): Category (architecture, development, documentation, testing, security, performance, product, design, research).
  • impact_level (string): Level of impact (high, medium, low).

ISSUE

Records an issue or problem encountered in the project memory.

• Arguments (guided by LLM):
  • description (string, required): Description of the issue.
  • severity (string, required): Severity level (critical, high, medium, low).
  • area (string, required): Area affected (product, technical, process, security, performance).

DECISION

Records a technical or architectural decision in the project memory.

• Arguments (guided by LLM):
  • description (string, required): Description of the decision.
  • impact (comma-separated): Areas impacted (architecture, development, documentation, testing, security, performance, product, design, process).
  • rationale (string, required): Reasoning behind the decision.

MEMORY

Shows the development record (achievements, issues, decisions).

• Arguments (guided by LLM):
  • filter (string): Filter memory by category (all, achievements, issues, decisions, product, technical).
  • format (string): Output format (detailed, summary, timeline).

CODING_STYLE

Manages programming style guides for the project.

• Arguments (guided by LLM):
  • operation (string, required): Style operation (apply, customize, list, show).
  • style_name (string, required): Name of the style (frontend, backend, chrome_extension, tdd, github_workflow).
  • project_name (string, required): Name of the project to apply style to.
  • customizations (string): Path to customization file or inline JSON customizations.

GENERATE_CONTEXT

Converts a code repository into a text format suitable for LLM processing.

• Arguments (guided by LLM):
  • repo_path (string, required): Path to the repository to process.
  • output (string): Output file name for the extracted content.
  • line_numbers (boolean): Whether to show line numbers in the output file.
  • style (string): Output style (plain, xml).
  • ignore (string): Additional patterns to ignore (comma-separated).

GENERATE-DOC

Analyzes code (or existing context) and generates documentation interactively.

• Arguments (guided by LLM):
  • repo_path (string): Path to the repository to analyze (optional if context already exists).
  • context_path (string): Path to the context directory (default: code_context/extracted).
  • output (string): Path where to save the generated documentation.
  • template (string): Documentation template to use.

📊 MECE Documentation Structure

Our AI-First taxonomy ensures complete context preservation: