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These docs are built to be read by AI assistants and agents, not just browsers. Every page is available as plain markdown, the whole site publishes a machine-readable index, and a hosted MCP server exposes search over the content. This page covers the three ways to put them to work: reading the docs as an agent, connecting the hosted MCP server, and starting from curated prompts.

Read the Docs as an Agent

The Documentation Index: llms.txt

The site publishes an llms.txt index at:
It is a plain-markdown listing of the site’s pages with their URLs. Point an assistant at it to discover what exists before fetching individual pages. This is the recommended entry point for any agent working with these docs.

Per-Page Markdown

Every page is served as raw markdown at its own URL with .md appended. For example, the Node Operator Overview page is available as:
Fetching the .md form skips the HTML shell, navigation, and scripts, so an assistant gets exactly the content of the page in a fraction of the tokens. Prefer it over the HTML URL whenever your tooling fetches pages programmatically.

The Contextual Menu

Every page in these docs has a contextual menu (next to the page title) with assistant-ready actions:
  • Copy page: copies the page as markdown, ready to paste into any chat.
  • Open in ChatGPT: opens a ChatGPT conversation preloaded with the page.
  • Open in Claude: opens a Claude conversation preloaded with the page.
  • Copy MCP server URL: copies the hosted MCP server address for your client configuration.
Use it when you are reading a page and want to hand it to an assistant without constructing URLs by hand.

Tips for Agents Reading These Docs

  • Start from llms.txt to map the site, then fetch only the .md pages you need.
  • These docs describe how to use the OP Stack. The normative protocol definition lives in the OP Stack specifications - when a docs page and the specs disagree, the specs win.
  • Configuration flags and versions change between releases. Confirm load-bearing values against the linked source or release notes before acting on them.

Connect the Hosted MCP Server

The Model Context Protocol (MCP) is an open standard for connecting AI assistants to external tools and data sources. These docs are hosted on Mintlify, which automatically generates and hosts an MCP server for the site. The server exposes a search tool over the documentation, so a connected assistant (Claude Code, Claude Desktop, Cursor, and others) can look up current OP Stack content instead of relying on stale training data. The docs MCP server is available over HTTP at:
1

Add the server to your client

For Claude Code, run:
For Cursor, Claude Desktop, and other clients that use a JSON configuration, add the server URL:
You can also connect from any page of these docs: the contextual menu (next to the page title) includes an option to copy the MCP server details for your client.
2

Verify it works

Ask your assistant:
A correctly wired assistant calls the server’s search tool, locates Building and Running an OP Stack Node From Source, and answers from the live page.

Option 2: Direct Fetching (No MCP Server Required)

If your assistant can fetch web pages but does not support MCP, no setup is needed. The site’s machine-readable surfaces - the llms.txt index and per-page markdown - work with any assistant that can fetch URLs. Give it the index and the URL convention:
Add that to your assistant’s project instructions (for example CLAUDE.md, .cursorrules, or a system prompt) and it will resolve OP Stack questions against the live docs. If your tooling only reaches the network through MCP and cannot use the hosted server, the reference fetch server (uvx mcp-server-fetch) plus the instruction block above achieves the same result. If the hosted MCP endpoint is not responding, open an issue so we can investigate.

Prompt Starters

Copy-paste prompts for common OP Stack tasks, ready for any AI assistant that can fetch URLs. Each prompt names its intended persona, grounds the assistant in specific docs pages (fetched as per-page markdown), and tells it what to produce. Replace the bracketed placeholders with your own details before sending. If your assistant cannot fetch URLs, open the linked pages and use the contextual menu’s Copy page action to paste them in instead.

Audit My Batcher Configuration for Cost

Persona: chain operator Data availability is usually a chain’s largest onchain operating cost, and the batcher configuration controls it. Grounding pages: Configure the Batcher and Transaction Fees 101.

Walk Me Through the Deposit Flow

Persona: app developer Understand what actually happens between an L1 deposit call and the transaction appearing on L2 before you build on it. Grounding pages: Deposit Flow and Deposit Transactions.

Plan a Fault-Proof Challenger Deployment

Persona: chain operator Every permissionless fault-proof chain needs an honest challenger watching its dispute games. Grounding pages: OP-Challenger Explainer and How to Configure Challenger for Your Chain.

Bridge ETH From L1 in My App

Persona: app developer Move ETH between Ethereum and an OP Stack chain programmatically. Grounding page: Submitting Transactions From L1.

Explain My Transaction’s Fee Breakdown

Persona: app developer OP Stack transactions pay an execution fee and a data-availability fee; estimating only one of them causes bugs. Grounding page: Transaction Fees on OP Mainnet.

Debug a Stuck Withdrawal

Persona: app developer Withdrawals are multi-step and time-delayed by design; most “stuck” withdrawals are actually mid-flight. Grounding pages: Withdrawal Flow and Transaction Finality.

Contributing

The prompts above are curated: each one maps to a task readers actually arrive with, and each grounds the assistant in maintained docs pages. To propose a new prompt or fix a stale one, open an issue or edit this page.