🧠 PAELLADOC: The AI-First Development Framework

Version 0.3.3: This release focuses on fixing dependency issues for PyPI packaging and improving configuration documentation for the Model Context Protocol (MCP).

"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

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)

Installing via Smithery

To install PAELLADOC for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @jlcases/paelladoc --client claude

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 Database Path

PAELLADOC needs to know where to store its memory database (memory.db). There are two main ways to configure this:

Option 1: Environment Variable (Less Reliable for LLM Integration)

You can set the PAELLADOC_DB_PATH environment variable. This works well if you run PAELLADOC directly from your terminal.

# Example: Set the variable in your current terminal session
export PAELLADOC_DB_PATH="$HOME/.paelladoc/memory.db"

# Optional: Add the export line to your shell's startup file 
# (.bashrc, .zshrc, etc.) for it to persist across sessions.

Important: When PAELLADOC is run by an LLM tool (like Cursor via MCP), it might not inherit environment variables set this way. Therefore, this method is less reliable for LLM integration.

Option 2: MCP Configuration (Recommended for LLM Integration)

The most reliable way to ensure your LLM tool uses the correct database path is to configure it directly within the tool's MCP JSON file (.cursor/mcp.json for Cursor). This injects the variable directly into the server process launched by the LLM.

See the examples in the next section.

4. Configure Your LLM (MCP Setup)

Now, tell your LLM tool (like Cursor) how to find and run PAELLADOC.

Key Information Needed:

Full Path to Python Executable: Absolute path to python inside your .paelladoc_venv.

Cursor IDE Example

Edit your .cursor/mcp.json file. This is the minimum required configuration:

{
  "mcpServers": {
    "Paelladoc": {
      "command": "/absolute/path/to/.paelladoc_venv/bin/python", 
      "args": [
        "-m",
        "paelladoc.ports.input.mcp_server_adapter",
        "--stdio"
      ],
      "disabled": false
    }
  },
  "mcp.timeout": 120000
}

Important Notes:

The command path must be the absolute path to the Python executable created in Step 1 (e.g., /Users/your_username/.paelladoc_venv/bin/python). This is the only strictly required configuration.
By default, PAELLADOC will use ~/.paelladoc/memory.db for its database. If you need a custom location, you must add an "env" section inside the "Paelladoc": { ... } block, like this:

=======

// 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

Ensure PAELLADOC is installed (pip install paelladoc) and configured in your LLM's tool/MCP settings (see examples above).
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.
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 (v0.3.3)

This version provides the following core commands, exposed via MCP for interaction with your LLM:

ping:
- Description: Basic health check to confirm the server is running and responsive.
- Arguments: None (or optional random_string).
- Returns: { "status": "ok", "message": "pong" }.
paella_init:
- Description: Initializes a new PAELLADOC project, creating the necessary structure and initial memory file.
- Arguments: base_path (str), documentation_language (str, e.g., "es-ES"), interaction_language (str, e.g., "en-US"), new_project_name (str).
- Returns: Dictionary confirming project creation status, name, and path.
paella_list:
- Description: Lists the names of all existing PAELLADOC projects found in the memory database.
- Arguments: None.
- Returns: Dictionary containing a list of project names (projects).
paella_select:
- Description: Selects an existing PAELLADOC project to work on (loads its memory).
- Arguments: project_name (str).
- Returns: Dictionary confirming project selection and its base path.
core_continue:
- Description: Continues work on a previously selected project, loading its memory and suggesting next steps (basic implementation).
- Arguments: project_name (str).
- Returns: Dictionary with project status and suggested next step.
core_help:
- Description: Provides help information about available commands (basic stub implementation).
- Arguments: None (future: specific command).
- Returns: Placeholder success message.
core_list_projects:
- Description: (Likely redundant with paella_list) Lists the names of existing PAELLADOC projects.
- Arguments: db_path (str, optional, for testing).
- Returns: Dictionary containing a list of project names (projects).
core_verification:
- Description: Checks documentation quality and completeness (basic stub implementation).
- Arguments: None.
- Returns: Placeholder success message.

🗺️ Future Roadmap Highlights

Based on the Unified Roadmap, future versions aim to include:

Full interactive documentation generation flows (GENERATE-DOC).
Code analysis and context generation (GENERATE_CONTEXT).
Automatic code generation from documentation (code_generation).
Management of coding styles and Git workflows (styles.coding_styles, styles.git_workflows).
Project memory commands for decisions, issues, achievements (DECISION, ISSUE, ACHIEVEMENT).
And much more, aligning with the MECE taxonomy and A2A capabilities.

📊 MECE Documentation Structure

Our AI-First taxonomy ensures complete context preservation:

>>>>>>> main