Integrations

Supercharge More Trees with Relevance AI

More Trees is a platform that enables automated tree planting and carbon offset tracking via a simple API. By integrating with Relevance AI, users can leverage AI Agents to optimize their environmental efforts and streamline reporting.

Give your AI Agents More Trees Superpowers

More Trees empowers users to automate tree planting and track carbon offsets effortlessly. With Relevance AI, these capabilities are amplified, allowing for intelligent decision-making and real-time environmental impact management.

Environmental Impact Orchestration

Seamlessly coordinates and optimizes sustainability initiatives across multiple business touchpoints

Carbon Intelligence Amplification

Enhances decision-making by processing and analyzing real-time carbon offset data

Impact Verification Precision

Ensures accuracy in environmental impact tracking through blockchain-verified data validation

Tools

Equip AI Agents with the More Trees Tools they need

Relevance AI seamlessly integrates with More Trees to enhance your environmental initiatives through automated workflows.

More Trees - Get Carbon Offset
Retrieves carbon offset data from the More Trees platform, allowing users to track CO2 sequestration metrics for their tree planting initiatives
More Trees - Plant Tree
Facilitates tree planting through the More Trees platform, enabling users to plant trees either for themselves or others with specified tree types
Name
More Trees API Call
Description
Make an authorized request to a More Trees API
Parameters
["OAuth account authentication", "HTTP method selection (GET, POST, PUT, DELETE, PATCH)", "Custom request headers", "Request body configuration", "API endpoint path customization"]
Use Case
An eco-conscious e-commerce company uses the More Trees API to automatically plant trees for each customer purchase, tracking their environmental impact through API calls that create and monitor tree planting orders. This enables them to provide real-time sustainability metrics to customers while managing their reforestation initiatives.

Security & Reliability

The More Trees integration with Relevance AI enables seamless automated tree planting and carbon offset tracking through a straightforward API interface. This integration allows developers to programmatically plant trees, monitor carbon offsets, and manage environmental initiatives directly from their applications.

With automated tree planting capabilities and real-time carbon offset tracking, you can streamline your environmental impact reporting effortlessly. The integration employs OAuth-based secure authentication, ensuring that only authorized workflows can access your environmental data.

To get started, ensure you have the necessary accounts with More Trees and Relevance AI, along with valid OAuth credentials. Your environment should support HTTPS, JSON parsing, and OAuth 2.0.

Once set up, you can easily retrieve carbon offset data, plant trees, and make custom API calls. The integration also provides troubleshooting tips for common issues, such as authentication errors and invalid tree types, along with best practices for optimal performance.

For additional support or detailed API documentation, visit the More Trees API documentation or contact their support team.

No training on your data

Your data remains private and is never utilized for model training purposes.

Security first

We never store anything we don’t need to. The inputs or outputs of your tools are never stored.

Get Started

Best Practices for Non-Technical Users

To get the most out of the More Trees + Relevance AI integration without writing code:
  • Start with a valid OAuth setup: Ensure your OAuth credentials are correctly configured and have the necessary permissions.
  • Use the API documentation: Familiarize yourself with the More Trees API endpoints for planting trees and tracking carbon offsets.
  • Validate tree types: Use the remote_options endpoint to retrieve valid tree type slugs before making planting requests.
  • Test API calls: Conduct tests with sample data to ensure your integration works as expected before deploying it in a live environment.
  • Implement error handling: Prepare for common API errors by implementing robust error handling and logging mechanisms.