Everyone's trying to figure out how to make their APIs "AI-native." The current leading solution is MCP (Model Context Protocol) — a standardised way for LLMs to discover and interact with external tools.

But MCP is complicated. What if there was a simpler way?

The Problem with LLM Integration Today

MCP requires:

• Implementing a protocol
• SDKs and tooling
• Infrastructure overhead
• Another dependency to maintain

And here's the thing: it's solving a problem that doesn't need to be this hard.

A Simpler Proposal: The /context Endpoint

What if every API just exposed a single endpoint that returns an AI-ready explanation of how to use it?

GET /api/context/


That's it. One endpoint. Returns a document (markdown, plain text, whatever) that explains:

• What this API does
• How authentication works
• What endpoints are available
• How to perform common operations
• What the response formats look like
• Any gotchas or important patterns

No protocol. No SDK. No special tooling. Just HTTP.

How It Works

When an LLM needs to interact with your API, the first instruction is simple:

"Start by calling GET /context to understand how this API works."

The LLM reads the context, understands the API, and proceeds to make real calls. Self-documenting, always up-to-date, works with any LLM that can make HTTP requests.

Example: NetOrca Consumer Context

Here's what a context endpoint might return for an infrastructure orchestration platform:

# NetOrca Consumer Context

You are interacting with NetOrca, a declarative infrastructure orchestration platform.

## Authentication
Your API key is scoped to specific services.
Use header: `Authorization: Api-Key <key>`

## Check Current State
`GET /orcabase/consumer/submissions/` - List your current submissions

## Available Services  
`GET /orcabase/consumer/services/` - See services you can request
`GET /orcabase/consumer/services/{id}/schema/` - Get the schema for a service

## Making Requests
Submissions are declarative. Always send the full desired state.
`POST /orcabase/consumer/submissions/` with your complete YAML/JSON declaration.

Changes are detected automatically. Append new items or modify existing ones.

## After Submission
- Change instances are created for each detected change
- Changes go through approval workflow (if configured)
- Approved changes are deployed automatically

## Tips
- Always GET your current submission before modifying
- Send the complete file, not patches
- Check change instance status to track progress

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That's everything an LLM needs to start working with the API. No special integration required.

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