Features:

• Intelligent Query Analysis: Automatically identifies API platforms (OpenAI, Stripe, Bright Data, etc.) and operation types
• Smart Documentation Extraction: Uses advanced browser automation to scrape official API documentation
• Production-Ready Output: Generates clean documentation with cURL examples, authentication, error handling, and parameters
• Two Deployment Options:
  • Web UI: Beautiful Streamlit interface with real-time progress tracking
  • MCP Server: Direct integration with Cursor, Claude Desktop, and any MCP-compatible client
• Real-Time Processing: Watch each step execute live from query analysis to final documentation

Demo

Click to watch the full demo on YouTube

Architecture

Built with LangGraph for robust, scalable AI workflows:

graph LR
 A[ Natural Language Query] --> B[ Analyze Query]
 B --> C[ Generate Plan]
 C --> D[ Execute Browser]
 D --> E[ Generate Response]
 E --> F[ API Documentation]

 %% Deployment Options
 F --> G[ Streamlit Web UI]
 F --> H[ MCP Server]

 %% End Users
 G --> I[ Web Browser]
 H --> J[ Claude Desktop]
 H --> K[ Cursor IDE]

 %% Styling
 classDef process fill:#e1f5fe,stroke:#01579b,stroke-width:2px
 classDef output fill:#f3e5f5,stroke:#4a148c,stroke-width:2px
 classDef interface fill:#e8f5e8,stroke:#1b5e20,stroke-width:2px

 class B,C,D,E process
 class F output
 class G,H,I,J,K interface

Project Structure

EasyDocs/
 src/
 graph.py # LangGraph workflow definition
 nodes.py # Individual processing nodes
 state.py # State management schema
 app.py # Streamlit web interface
 main.py # Testing and validation suite
 mcp_wraper.py # MCP server wrapper
 test_single.py # Individual node testing
 .env.example # Environment variables template
 README.md # This file

Quick Start

Prerequisites

• Python 3.8+
• API keys for target platforms (OpenAI, Bright Data, etc.)

Installation

1. Clone the repository

   git clone https://github.com/MeirKaD/EasyDocs.git
   cd EasyDocs

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate # On Windows: venvScriptsactivate

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment

   cp .env.example .env
   # Edit .env with your API keys

Environment Variables

Create a .env file with the following variables:

# Google AI (for LLM processing)
GOOGLE_API_KEY=your_google_ai_api_key

# Bright Data (for web scraping)
BRIGHT_DATA_API_TOKEN=your_bright_data_token
WEB_UNLOCKER_ZONE=unblocker
BROWSER_ZONE=scraping_browser

Usage

Option 1: Web Interface

Launch the Streamlit app for an interactive web experience:

streamlit run app.py

Features:

• Clean input interface with example queries
• Real-time progress tracking
• Confidence indicators and debug information
• Download generated documentation
• Professional UI with responsive design

Example Queries:

• “How to create a payment intent with Stripe API”
• “How to send a POST request to OpenAI’s chat completion API”
• “How to authenticate with Bright Data’s Web Scraper API”

Option 2: MCP Server Integration

Transform your coding workflow by integrating directly with your development environment.

Setup with Claude Desktop

1. Add to Claude Desktop configuration (claude_desktop_config.json):

   {
    "mcpServers": {
    "EasyDocs": {
    "command": "/path/to/your/EasyDocs/venv/bin/python",
    "args": ["/path/to/your/EasyDocs/mcp_wraper.py"]
    }
    }
   }

2. Update paths to match your installation directory

3. Restart Claude Desktop

4. Use in conversations:

   @EasyDocs generate API documentation for creating a Stripe payment intent

Setup with Cursor

1. Install MCP extension in Cursor
2. Add server configuration pointing to mcp_wraper.py
3. Use directly in your code editor for instant API documentation

Test MCP Server

Verify your MCP server is working:

# Test with MCP Inspector
npx @modelcontextprotocol/inspector mcp_wraper.py

# Or test directly
python mcp_wraper.py

Testing

Run Full Test Suite

python main.py

This will:

• Validate graph compilation
• Test query analysis accuracy
• Verify step-by-step execution
• Check final documentation quality

Test Individual Components

# Test specific nodes
python test_single.py

# Test with custom query
python -c "
from src.graph import create_demo_graph
graph = create_demo_graph()
result = graph.invoke({
 'query': 'Your test query here',
 'platform': '', 'action_plan': [], 'extracted_content': '',
 'final_response': '', 'error': None, 'operation_type': '',
 'confidence': 0.0, 'estimated_duration': 0, 'complexity_level': '',
 'current_step': 0, 'confidence_level': None, 'explanation': None
})
print(result['final_response'])
"

API Reference

Core Functions

analyze_query(state: DemoState) -> DemoState

Analyzes user queries to extract platform and operation type.

Input: Query string in natural language
Output: Platform identification, operation type, confidence score

generate_plan(state: DemoState) -> DemoState

Creates step-by-step action plan for documentation extraction.

Output: Action steps, estimated duration, complexity level

execute_browser(state: DemoState) -> DemoState

Executes browser automation to extract API documentation.

Output: Extracted content, confidence level, processing explanation

generate_response(state: DemoState) -> DemoState

Generates developer-friendly documentation with examples.

Output: Formatted documentation with cURL commands, authentication, parameters

MCP Tool

generate_api_docs(question: str) -> str

Main MCP tool for generating API documentation.

Parameters:

• question (string): Natural language query about API documentation needs

Returns: Formatted API documentation with examples and best practices

Tech Stack

• Langgraph
• MCPuse
• Gemini
• FastMCP
• Streamlit

Troubleshooting

Common Issues

“Module not found” with MCP server:

# Ensure virtual environment path is correct
which python # Use this path in Claude Desktop config

Browser execution fails:

# Check Bright Data credentials
echo $BRIGHT_DATA_API_TOKEN

Low confidence documentation:

• Try more specific queries
• Include platform name explicitly
• Check if platform is supported

Debug Mode

Enable detailed logging:

# In src/nodes.py, add:
import logging
logging.basicConfig(level=logging.DEBUG)

Contributing

We welcome contributions! Here’s how to get started:

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Add tests for new functionality
4. Run test suite: python main.py
5. Submit pull request

License

This project is licensed under the MIT License – see the LICENSE file for details.

Support

• Documentation: Check this README and inline code comments
• Issues: Open a GitHub issue for bugs or feature requests
• Discussions: Use GitHub Discussions for questions and ideas

Ready to transform your API development workflow? Start with the web interface, then integrate the MCP server for seamless coding productivity!

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