> ## Documentation Index
> Fetch the complete documentation index at: https://checklyhq.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Agents & Coding Assistants

> Use AI agents and coding assistants with Checkly Skills and the Checkly MCP Server.

From the beginning, Checkly has bet on [Monitoring as Code](/concepts/monitoring-as-code) which lets you create and control your monitoring infrastructure entirely using code.

By default, [Checkly constructs](/constructs/overview) reflect all your monitoring properties.

```ts api.check.ts theme={null}
import { ApiCheck, AssertionBuilder } from "checkly/constructs"

new ApiCheck("api-health-check", {
  name: "API Health Check",
  request: {
    url: "https://danube-web.shop/api/books",
    method: "GET",
    assertions: [
      AssertionBuilder.statusCode().equals(200),
    ],
  },
})
```

All your monitoring resources can be updated, tested and deployed via [the Checkly CLI](/cli/overview).

```bash theme={null}
# test your monitoring configuration
npx checkly test

# deploy and update your monitoring setup
npx checkly deploy
```

**The Monitoring as Code workflow is by default AI-native** because LLMs are excellent at writing and editing Checkly constructs code and modern AI agents can execute CLI commands easily.

Provide the necessary Checkly context and let your AI agent of choice do the rest.

<Card title="Create new checks, alert channels or other constructs" horizontal>
  "Can you set up a new `BrowserCheck` for `example.com`?"
</Card>

<Card title="Bulk-update your monitoring resources" horizontal>
  "Can you change all checks to run every 5 minutes instead of every 10 minutes?"
</Card>

<Card title="Gather information about alerts and your monitoring setup" horizontal>
  "I just received an alert. Can you tell me details about the failing checks?"
</Card>

<Card title="Handle and communicate incidents" horizontal>
  "Can you please open an incident and investigate a fix?"
</Card>

## Add Checkly context to your AI agent conversation

Install [Checkly Skills](/ai/skills) or connect the [Checkly MCP Server](/ai/mcp-server) to give your AI agent enough context to perform Checkly-related tasks.

<Columns cols={2}>
  <Card title="Checkly Skills" href="/ai/skills" cta="Get started">
    For coding agents with repo and code access. Author Monitoring as Code with the Checkly CLI.
  </Card>

  <Card title="Checkly MCP Server" href="/ai/mcp-server" cta="Connect">
    For desktop or sandboxed agents. Live access to Checkly account data and remote actions.
  </Card>
</Columns>

### Skills, MCP, and the CLI

**Use Skills for a CLI-first workflow.** Skills are built for coding agents that have access to your repository and can run commands. The agent edits your Checkly constructs and tests, then uses the [Checkly CLI](/cli/overview) to test and deploy them. Skills load context on demand, keeping your agent's context window lean until Checkly-related tasks arise. This is the recommended approach for agents that support the [Agent Skills](https://agentskills.io) standard.

**Use the MCP Server in desktop or sandboxed environments.** Some agents, like Claude Desktop, ChatGPT, or a sandboxed assistant, can't access your repository or filesystem. The [Checkly MCP Server](/ai/mcp-server) connects these clients to Checkly over Streamable HTTP and gives them live data and remote account actions, such as reading check status, inspecting test sessions, triggering existing checks, reading RCA, or managing status page incidents.

| Use case                                                    | Recommended path                                                   |
| ----------------------------------------------------------- | ------------------------------------------------------------------ |
| Ask quick, ad hoc questions from a supported client         | [MCP Server](/ai/mcp-server)                                       |
| Inspect live Checkly data without local CLI setup           | [MCP Server](/ai/mcp-server)                                       |
| Trigger existing deployed checks from chat                  | [MCP Server](/ai/mcp-server) or [Checkly CLI](/cli/overview)       |
| Create or edit check code                                   | [Checkly Skills](/ai/skills) with the [Checkly CLI](/cli/overview) |
| Test, deploy, or automate local Monitoring as Code projects | [Checkly CLI](/cli/overview)                                       |
| Add reusable Checkly best practices to an agent             | [Checkly Skills](/ai/skills)                                       |

## Feed documentation to your agent

Every Checkly documentation page is available as markdown, and an `llms.txt` index lists them all. Both make it easy to pull the exact docs your agent needs into its context.

<Columns cols={2}>
  <Card title="Markdown Access" href="/ai/markdown-access" cta="Learn more">
    Append `.md` to any docs URL, request markdown with content negotiation, or copy a page as markdown.
  </Card>

  <Card title="llms.txt" href="/ai/llms-txt" cta="Learn more">
    A machine-readable index of every documentation page for crawling and indexing.
  </Card>
</Columns>

## Let Checkly's AI run the monitor

The tools above help your AI agent author Monitoring as Code. If you'd rather describe a monitoring goal in plain language and let Checkly's AI run it for you, use Agentic Checks. They turn a prompt into a synthetic check that explores your app, evaluates assertions, and self-heals when the underlying flow changes.

<Card title="Agentic Checks" href="/detect/synthetic-monitoring/agentic-checks/overview" cta="Get started">
  Turn a prompt into an AI-powered synthetic check that discovers, verifies, and maintains itself.
</Card>