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OverviewMCP ServerQuickstartWhy ExpandWays to Use Expand

Fetch

OverviewHow Fetch WorksOutput ModelProgressive DisclosureInclude OptionsDocumentsYouTubeBrowser BehaviorHighlightsPlayground & ReplayBatched Fetch

Reference

MCP Tools & ResourcesCLI, Skill & HooksCLI CommandsAPI ReferenceTypeScript SDK

Account & Billing

Pricing & UsageTiersFAQ

Machine-Readable Docs

start.mdllms.txtllms-full.txtDocs as Markdown
Browse docs

Get Started

OverviewMCP ServerQuickstartWhy ExpandWays to Use Expand

Fetch

OverviewHow Fetch WorksOutput ModelProgressive DisclosureInclude OptionsDocumentsYouTubeBrowser BehaviorHighlightsPlayground & ReplayBatched Fetch

Reference

MCP Tools & ResourcesCLI, Skill & HooksCLI CommandsAPI ReferenceTypeScript SDK

Account & Billing

Pricing & UsageTiersFAQ

Machine-Readable Docs

start.mdllms.txtllms-full.txtDocs as Markdown

Ways to Use Expand

Choose the right Expand surface for agents, CLI, SDKs, API, or Playground.

Pick the path that matches how you want to use Fetch.

Use Expand from an agent, terminal, app, API workflow, or visual Playground. Each path gives you one useful next action and a canonical docs link.

The choice is never MCP versus CLI versus SDK. The product is Fetch. These are just different ways to call it from where you already work, and they all return the same output: Main Markdown, State JSON when useful, citations, and snapshot handles.

Match the path to where you are right now. Inside an agent, take the agent path so it fetches the web automatically. Testing by hand, run one CLI command. Building a product, call the SDK or the API and hand the same result to your own users. Want to see the evidence behind a Fetch? Open the Playground.

Recommended for agents

Agents

Coding agents, harnesses, and MCP clients that should use Fetch automatically.

On This Page

Pick the path that matches how you want to use Fetch.Compare pathsMCP belongs under agentsCommon workflowsMachine-readable entrypoints
Add https://api.expand.ai/v1/mcp to your agent.
Add the MCP server

CLI

Terminal fetches, scripts, local smoke tests, and repeatable workflows.

expandai fetch URL
CLI Commands

TypeScript SDK

Integrating Fetch into TypeScript applications.

@expandai/sdk
TypeScript SDK

API

Custom backends, other languages, and direct HTTP integrations.

POST /v1/fetch
API Reference

Playground

Visually inspecting snapshots, source evidence, screenshots, and Replay.

Open /dashboard
Playground & Replay

Compare paths

The cards above route you out. This table is the side-by-side check before you commit.

PathBest forSetupAuthCanonical docs
AgentsCoding agents and MCP clientsHosted MCP server, one URLBrowser sign-in or API key/docs/get-started/mcp-server
CLITerminal fetches and scriptsPublish pending; see CLI docsCLI login or EXPAND_API_KEY/docs/reference/cli-commands
TypeScript SDKTypeScript appsPackage installEXPAND_API_KEY/docs/reference/typescript-sdk
APICustom backends and other languagesHTTP clientx-expand-api-key/api-reference
PlaygroundVisual inspection and ReplayDashboard

MCP belongs under agents

MCP is one agent integration path, not a separate product. Use Agent Quickstarts for setup and MCP Tools & Resources for exact tool and resource names, such as fetch_search for Highlights.

Hooks, skills, and MCP combine. Hooks improve a client's native web-fetch behavior, skills teach the agent when to reach for Expand, and MCP exposes Fetch as callable tools. The agent pages give the recommended default for each client, so you do not have to assemble it yourself.

Common workflows

  • Give web context to an agent → Agents
  • Fetch from a terminal → CLI Commands
  • Build Fetch into a product → TypeScript SDK or API Reference
  • Inspect evidence visually → Playground & Replay

Machine-readable entrypoints

Point an agent or IDE tool at these routes when you want it to bootstrap from the docs instead of scraping the site:

  • start.md: setup instructions an agent can read and follow.
  • llms.txt: an index of the docs.
  • llms-full.txt: the full docs bundled into one file.

Major AI crawlers do not fetch llms.txt on their own. These routes pay off when you explicitly hand the link to an agent, so it loads accurate, current instructions in one step.

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