> ## Documentation Index
> Fetch the complete documentation index at: https://docs.andiai.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Content retrieval
> Fetch and extract clean content from any web page with the /api/v1/fetch endpoint.
The fetch endpoint extracts clean, structured content from a web page. You get the page title, description, full text, and metadata — without writing a scraper. It returns JSON by default, or LLM-ready markdown with `format=context`.
```bash theme={null}
curl "https://api.andiai.com/api/v1/fetch?url=https://example.com/article" \
-H "x-api-key: YOUR_API_KEY"
```
## Parameters
| Parameter | Type | Default | Description |
| ------------------ | ------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `url` | string | **required** | Absolute URL to fetch (http or https) |
| `format` | string | `json` | `json` or `context` (markdown with YAML frontmatter) |
| `effort` | string | — | How thoroughly to retrieve the page: `low`, `medium`, `high`, `max`. Omit for the adaptive default. See [using the effort parameter](#using-the-effort-parameter). |
| `maxContentLength` | integer | 200000 | Maximum content length in characters. You can lower this, not raise it. |
| `query` | string | — | When set, the response includes `query_extracts` and `query_snippet` scoped to this query. |
## JSON response
```json theme={null}
{
"url": "https://example.com/article",
"title": "Understanding Retrieval-Augmented Generation",
"description": "RAG combines retrieval with generation for grounded LLM responses.",
"content": "Retrieval-Augmented Generation (RAG) is a technique that...",
"markdown": "# Understanding Retrieval-Augmented Generation\n\nRAG is a technique...",
"extracts": [
"RAG retrieves relevant documents from a knowledge base before generating a response.",
"This grounds the model's output in factual, up-to-date information."
],
"snippet": "RAG combines retrieval with generation for grounded LLM responses.",
"author": "Jane Smith",
"date": "2026-06-15",
"site_name": "Example Blog",
"image": "https://example.com/images/rag-diagram.png",
"lang": "en",
"word_count": 1850,
"truncated": false,
"metrics": {
"duration_ms": 1240,
"cost_dollars": 0.001
}
}
```
Fields like `author`, `date`, `image`, and `site_name` appear when the page provides them.
## Context format
With `format=context`, the response is markdown with YAML frontmatter — ready to pass into an LLM context window:
```bash theme={null}
curl "https://api.andiai.com/api/v1/fetch?url=https://example.com/article&format=context" \
-H "x-api-key: YOUR_API_KEY"
```
```yaml theme={null}
---
title: Understanding Retrieval-Augmented Generation
url: https://example.com/article
description: RAG combines retrieval with generation for grounded LLM responses.
author: Jane Smith
date_published: 2026-06-15
source: example.com
lang: en
word_count: 1850
cost_dollars: 0.001
retrieved_at: 2026-07-14T21:30:00Z
---
Retrieval-Augmented Generation (RAG) is a technique that...
```
## Using the `query` parameter
Pass a `query` to get passage-level extracts scoped to a specific question:
```bash theme={null}
curl "https://api.andiai.com/api/v1/fetch?url=https://example.com/article&query=how+does+RAG+reduce+hallucination" \
-H "x-api-key: YOUR_API_KEY"
```
The response includes `query_extracts` (passages relevant to the query), `query_snippet` (a query-focused summary), and `query_hash`.
## Using the `effort` parameter
Pass `effort` to control how thoroughly the page is retrieved — the same tier names used by [search modes](/search/search-modes#the-effort-parameter):
```bash theme={null}
curl "https://api.andiai.com/api/v1/fetch?url=https://example.com/article&effort=high" \
-H "x-api-key: YOUR_API_KEY"
```
`low` favors speed; `max` spends the most time extracting content, useful for pages that are slow to load or render content client-side. Omit `effort` to use the server's adaptive default. Invalid values return a `400` listing the valid tiers.
Fetch failures (`422`, `503`) are never billed, regardless of `effort`.
## Partial responses
A `200` response can include partial content when the page is slow to fully retrieve:
| Field | Type | Description |
| --------------------- | ------- | ---------------------------------------------------------------------------- |
| `partial` | boolean | `true` when the returned content is a best-effort excerpt, not the full page |
| `retry_after_seconds` | integer | Seconds to wait before re-requesting the full document |
Treat `partial: true` as incomplete — re-request the same URL after `retry_after_seconds` to get the full content. This differs from a `503`, where no content was extracted at all.
## Error handling
The fetch endpoint uses two distinct error codes for page-level failures:
| Status | Meaning | What to do |
| ------- | -------------------------------------------------------------- | ------------------------------------------ |
| **503** | Page is still being retrieved, or a transient failure occurred | Retry after the `Retry-After` header value |
| **422** | Page was reached but content could not be extracted | Do not retry — the page is not extractable |
Both 422 and 503 responses are **not billed**. You only pay for successful extractions.
Other errors (400 for a missing or invalid URL, 401, 402, 429) follow the same patterns as the search endpoint — see [error handling](/resources/error-handling).
## Pricing
Fetch calls are billed at a flat $0.001 base per request, plus $0.05 per 1M tokens of extracted content. Failed fetches (422 and 503) are free.
The billed amount is returned in `metrics.cost_dollars` (JSON) or `cost_dollars` in the frontmatter (`format=context`).
## Search extracts as an alternative
If you already have search results and want passage-level text without fetching each page, use `extracts=true` on the search endpoint instead. This returns text extracts inline on each result at no additional cost:
```bash theme={null}
curl "https://api.andiai.com/api/v1/search?q=how+does+RAG+work&extracts=true" \
-H "x-api-key: YOUR_API_KEY"
```
The fetch endpoint is for when you need the full page content, or when you want to retrieve a specific URL that may not appear in search results.
## MCP tool
The `andi_fetch_url` MCP tool wraps this endpoint. See [Build with AI agents](/resources/ai-agents) for setup instructions.
## Next steps
Automatic effort by default, manual control when you want it.
Full parameter reference including `extracts`.
JSON and context format output structure.
MCP server and agent integration.