> For the complete documentation index, see [llms.txt](https://argos-ci.com/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://argos-ci.com/docs/agents/agents.md).

# Argos for AI agents

Everything Argos offers to AI agents: an MCP server, a CLI, installable agent skills, and AI-ready documentation.

Argos is built to work with AI agents. Whether your assistant reviews visual changes in a pull request, inspects builds from your terminal, or answers questions about Argos, these are the features it can use.

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>MCP server</strong></td><td>Connect AI assistants like Claude, Cursor, or Copilot to Argos: list builds, inspect diffs, review changes, and manage projects through the Model Context Protocol.</td><td><a href="/docs/agents/mcp-server.md">MCP server</a></td></tr><tr><td><strong>CLI</strong></td><td>Upload screenshots, inspect builds, and submit reviews from scripts and local workflows — the tool agents use when they work in your terminal.</td><td><a href="/docs/reference/argos-command-line-interface-cli.md">CLI</a></td></tr><tr><td><strong>Agent skills</strong></td><td>Installable skills that teach your assistant the Argos CLI and a complete pull-request visual review workflow.</td><td><a href="/docs/agents/agent-skills.md">Agent skills</a></td></tr><tr><td><strong>AI-ready docs</strong></td><td>This documentation is optimized for LLMs: Markdown for every page, <code>llms.txt</code> indexes, and its own docs MCP server.</td><td><a href="/docs/agents/ai-ready-docs.md">AI-ready docs</a></td></tr></tbody></table>

### Where to start

* To review builds with an AI agent in a pull request, follow the [Review builds with AI agents](/docs/learn/review-workflow/review-builds-with-ai-agents.md) guide — it combines the CLI and agent skills.
* To let an agent track down what makes a screenshot unstable, follow [Fix flaky tests with AI agents](/docs/learn/reliability-and-flakiness/fix-flaky-tests-with-ai-agents.md).
* To give a general-purpose assistant access to your Argos account, connect it to the [MCP server](/docs/agents/mcp-server.md).
* To let an agent answer questions about Argos itself, point it at the [AI-ready docs](/docs/agents/ai-ready-docs.md).


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://argos-ci.com/docs/agents/agents.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.