What is llms.txt and Why Your Developer Tools Need One

There’s a new file quietly showing up in developer tools: llms.txt. If you haven’t encountered it yet, you will. It’s a plain-text file designed to make your product understandable to AI coding assistants — and it’s becoming the new README for the age of AI-assisted development.

What is llms.txt?

llms.txt is a structured plain-text file that describes a product, API, or tool in a format optimized for large language models. Think of it as a robots.txt for AI assistants — but instead of telling crawlers what to avoid, it tells AI assistants how to use your product.

A typical llms.txt file includes:

• What the product does — a concise description
• API endpoints — methods, URLs, request/response formats
• Authentication — how to get and use API keys
• Examples — common use cases with working code
• Constraints — rate limits, pricing, gotchas

Here’s a simplified example:

# Postbox

Postbox is a structured data collection API.
Define a schema, get an endpoint, collect data.

## Authentication

All API requests require a Bearer token.
Get your API key from the dashboard.

## Create a Form

POST /api/forms
Authorization: Bearer {api_key}
Content-Type: application/json

{
  "name": "Contact",
  "fields": [
    { "name": "email", "type": "email", "required": true },
    { "name": "message", "type": "textarea" }
  ]
}

## Submit Data

POST /api/{hash}/f/{slug}
Content-Type: application/json

{ "email": "user@example.com", "message": "Hello!" }

When a developer pastes this into Claude, Cursor, or Windsurf, the AI immediately understands how to work with the API. No documentation site to crawl. No hallucinated endpoints. Just the facts, in a format the AI can act on.

Why This Matters Now

The way developers discover and integrate tools is shifting. A year ago, the workflow was: find a library, read the docs, copy an example, adapt it to your project. Today, an increasing number of developers describe what they want to an AI assistant and let it figure out the integration.

This creates a problem. AI assistants are working from training data that might be months or years old. They hallucinate API endpoints. They use deprecated methods. They miss breaking changes. The docs on your website are written for humans, and AI assistants do a mediocre job of extracting structured information from them.

llms.txt solves this by giving AI assistants a canonical, up-to-date source of truth. It’s:

• Always current — you update it alongside your API
• Structured for machines — no marketing copy, no navigation, just the information AI needs
• Portable — a developer can paste it into any AI tool
• Lightweight — a single file, not an entire documentation site

llms.txt as a Distribution Channel

Here’s the insight most people miss: llms.txt isn’t just a documentation format. It’s a distribution channel.

When a developer pastes your llms.txt into their AI assistant, they’re giving the AI permission and instructions to use your product. The AI becomes a zero-friction integration layer between the developer’s intent and your API.

Consider the funnel:

1. Developer has a problem (“I need a contact form backend”)
2. Developer finds your product
3. Developer pastes your llms.txt into their AI assistant
4. AI creates a working integration in minutes
5. Developer ships

Steps 3–5 happen in a single conversation. No signup friction. No documentation maze. No “getting started” tutorial. The llms.txt file carries enough context for the AI to go from zero to working integration.

For developer tools, this means the quality of your llms.txt directly affects your activation rate. A great llms.txt file converts “I found this tool” into “I’m using this tool” in minutes instead of hours.

What Makes a Good llms.txt

Not all llms.txt files are created equal. Through building Postbox’s own llms.txt and watching how developers use it, here’s what works:

Start with a one-sentence description. The AI needs to understand what your product does before it can use it. “Postbox is a structured data collection API” is better than three paragraphs of marketing copy.

Include authentication upfront. Every AI assistant will need to make API calls. Put auth information early so the AI doesn’t generate code that hits unauthenticated endpoints.

Use real examples, not pseudocode. Show complete request/response pairs. AI assistants copy patterns — give them good ones.

Document the happy path first. Don’t lead with edge cases. Show the simplest way to accomplish the most common task. The AI can handle variations once it understands the basics.

Keep it updated. A stale llms.txt is worse than no llms.txt. If your API changes, update the file. If you deprecate an endpoint, remove it.

How Postbox Uses llms.txt

At Postbox, llms.txt is a core part of the product, not an afterthought. Every user gets a personalized llms.txt file from their dashboard that includes:

• Their API key (so the AI can make authenticated requests)
• Their existing forms and schemas
• Endpoint URLs with their account hash
• Examples tailored to their setup

The workflow: a developer copies their llms.txt, pastes it into Claude or Cursor, and says “Add a contact form to my site.” The AI reads the file, understands the API, and generates working code — with the developer’s actual credentials and endpoints baked in.

This isn’t a hypothetical future. It’s how a growing number of Postbox users build their integrations today.

The Bigger Picture

llms.txt is part of a broader shift toward AI-native developer tools. robots.txt told search engines how to crawl your site. llms.txt tells AI assistants how to use your product. MCP (Model Context Protocol) takes it further, letting AI assistants interact with your API directly through a standardized protocol.

These aren’t competing standards — they’re layers. llms.txt is the static context layer (paste and go). MCP is the dynamic interaction layer (live API access). Together, they make your product a first-class citizen in AI-assisted workflows.

If you’re building developer tools, the question isn’t whether to support llms.txt — it’s how soon. The developers who adopt AI assistants first are often the same developers who adopt your product first. Meeting them where they work means meeting them in the AI assistant.

Getting Started

If you want to create an llms.txt for your own product:

1. Start with a clear, one-sentence description
2. Document your authentication method
3. List your core endpoints with request/response examples
4. Include 2-3 complete workflow examples
5. Host it at yoursite.com/llms.txt and in your dashboard

If you want to see llms.txt in action, sign up for Postbox. Your personalized llms.txt is waiting in the dashboard. Paste it into your AI assistant and see what happens.