MCP-Odoo

Model Context Protocol server for Odoo integration, allowing AI agents to access and manipulate Odoo data through a standardized interface.

Overview

MCP-Odoo provides a bridge between Odoo ERP systems and AI agents using the Model Context Protocol (MCP). This enables AI systems to:

• Access partner information

• View and analyze accounting data including invoices and payments

• Perform reconciliation of financial records

• Query vendor bills and customer invoices

Related MCP server: Odoo MCP Improved

Features

• 🔌 Easy integration with Odoo instances

• 🤖 Standard MCP interface for AI agent compatibility

• 📊 Rich accounting data access

• 🔒 Secure authentication with Odoo

Installation

# Clone the repository
git clone https://github.com/yourtechtribe/model-context-protocol-mcp-odoo.git
cd model-context-protocol-mcp-odoo

# Install dependencies
pip install -r requirements.txt

Configuration

Create a .env file in the project root with the following variables:

ODOO_URL=https://your-odoo-instance.com
ODOO_DB=your_database
ODOO_USERNAME=your_username
ODOO_PASSWORD=your_password
HOST=0.0.0.0
PORT=8080

Usage

Start the MCP server:

# Using the SSE transport (default)
python -m mcp_odoo_public

# Using stdio for local agent integration
python -m mcp_odoo_public --transport stdio

Documentation

Comprehensive documentation is available in the docs/ directory:

• Documentation Home - Start here for an overview of all documentation

• Implementation Guide - Detailed architecture and implementation details

• Accounting Functionality - In-depth guide to accounting features

• Troubleshooting - Solutions for common issues

• Usage Examples - Practical examples to get started

Development

Project Structure

• mcp_odoo_public/: Main package

  • odoo/: Odoo client and related modules

  • resources/: MCP resources definitions (tools and schemas)

  • server.py: MCP server implementation

  • config.py: Configuration management

  • mcp_instance.py: FastMCP instance definition

Adding New Resources

Resources define the capabilities exposed to AI agents through MCP. To add a new resource:

1. Create a new file in the resources/ directory

2. Define your resource using the @mcp.tool() decorator

3. Import your resource in resources/__init__.py

For detailed instructions, see the Implementation Guide.

License

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

Author

Albert Gil López

• Email: albert.gil@yourtechtribe.com

• LinkedIn: https://www.linkedin.com/in/albertgilopez/

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.