At AddressZen, we're making our services and documentation more LLM-friendly to help our customers more easily interact and integrate with us. This blog post outlines some of the steps we've taken to do this.
llms.txt for Instant Context
We've implemented a dedicated llms.txt file at docs.addresszen.com/llms.txt that provides complete context about AddressZen's services and APIs in a format optimized for large language models.
This single file contains everything an LLM needs to understand our platform:
- Complete API reference with examples
- Integration patterns and best practices
- Common use cases and implementation strategies
- Authentication and rate limiting details
Simply feed this file directly into your LLM to accelerate integration understanding and development. No more parsing through multiple documentation pages or explaining context repeatedly.
Markdown Documentation for Every Page
Every document on our documentation site can now be accessed as clean markdown by simply adding .md to any URL. For example:
https://docs.addresszen.com/docs/integrations/elementorbecomeshttps://docs.addresszen.com/docs/integrations/elementor.md
This markdown format is perfect for LLM consumption, removing HTML formatting and providing clean, structured content that AI models can easily parse and understand. Whether you're using GitHub Copilot, ChatGPT, or any other AI coding assistant, you can now quickly grab documentation in the most AI-friendly format possible.
Context7 MCP Integration
We've integrated with Context7 to make AddressZen documentation instantly accessible from AI-enabled IDEs. Our complete documentation has been indexed and is available through the Context7 MCP (Model Context Protocol).
To use this in your AI-powered development workflow:
- Install the Context7 MCP in your compatible IDE
- Reference it in your prompts: "How do I add AddressZen address lookup to this HTML form? Use context7"
- Get instant, contextually-aware responses with the most current AddressZen documentation
This integration means you no longer need to leave your IDE to find implementation details or troubleshoot integration issues. The AI assistant has direct access to our most up-to-date documentation and can provide specific, actionable guidance.
Our Context7 documentation listing can be found at https://context7.com/llmstxt/addresszen_llms_txt.
Streamlined OpenAPI Specification
We've completely optimized our OpenAPI specification to reduce context length while maintaining full functionality. The streamlined spec is available at:
- Web Interface: openapi.addresszen.com
- JSON Format: openapi.addresszen.com/openapi.json
- YAML Format: openapi.addresszen.com/openapi.yaml
Key improvements include:
- Reduced verbosity minimizes token usage while preserving all essential information
- Better example coverage for common use cases
- Cleaner schema definitions that are easier for LLMs to interpret
- Optimized for code generation with clearer parameter descriptions
This means when you ask an LLM to generate API integration code, it can work with a more focused, efficient specification that leads to better code generation results.
Why This Matters
We want to help developers be productive by meeting them where they work - in their IDEs, with their AI assistants, using the tools they are already familiar with.
The result is a development experience where:
- Context switching is minimized - documentation comes to your AI tools
- Integration time is reduced - LLMs have better context for code generation
- Troubleshooting is faster - AI assistants can provide specific, documented solutions
- Learning curves are flatter - comprehensive context helps AI explain concepts clearly
Getting Started
Try these improvements today:
- Feed docs.addresszen.com/llms.txt into your preferred LLM
- Use
.mdURLs for any documentation you want to include in AI prompts - Install Context7 MCP and reference it in your AI-powered IDE
- Point code generation tools at our streamlined OpenAPI spec
