> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.usescout.sh/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.usescout.sh/_mcp/server.

# Quickstart

## 1. Get an API key

Sign in at the [Scout dashboard](https://platform.usescout.sh) and create a
key under **Settings → API keys**. Keep it server-side — never embed it in
browser, mobile, or other untrusted code.

## 2. Send a request

Every endpoint accepts a JSON body over HTTPS. Pass your key as a Bearer
token in the `Authorization` header.

```bash cURL
curl -X POST https://core.usescout.sh/v1/search \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"queries": ["open source vector databases"], "limit": 5}'
```

```python Python
import requests

res = requests.post(
    "https://core.usescout.sh/v1/search",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={"queries": ["open source vector databases"], "limit": 5},
)
print(res.json())
```

```javascript JavaScript
const res = await fetch("https://core.usescout.sh/v1/search", {
  method: "POST",
  headers: {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    queries: ["open source vector databases"],
    limit: 5,
  }),
});
console.log(await res.json());
```

## 3. Read the response

Responses are flat JSON. A search returns a list of results, each with the
citation URL, a title, an excerpt, and an optional `publish_date`. Top-level
fields include a `search_id`, a `session_id` (echoed or generated) and a
`usage` breakdown by SKU. The `credits` field reports the relative cost of
the call.

```json Response
{
  "search_id": "search_4f5e...",
  "session_id": "sess_b8c1...",
  "queries": ["open source vector databases"],
  "results": [
    {
      "url": "https://example.com/vector-db-guide",
      "title": "A Guide to Open Source Vector Databases",
      "excerpts": [
        "Vector databases index high-dimensional embeddings for fast similarity search."
      ],
      "publishedDate": "2024-12-02",
      "publish_date": "2024-12-02"
    }
  ],
  "warnings": null,
  "credits": 1,
  "usage": [{ "name": "sku_search", "count": 1 }]
}
```

## Want more in one call?

Pass `advanced_settings.contents` to fetch each result's full text,
focused highlights and a per-result summary in a single request:

```json
{
  "queries": ["AI funding rounds 2026"],
  "objective": "latest Series A/B AI funding",
  "category": "news",
  "advanced_settings": {
    "source_policy": { "after_date": "2026-01-01" },
    "contents": {
      "text": true,
      "highlights": { "query": "funding amount, investors" },
      "summary":    { "query": "Who raised, how much, from whom?" }
    }
  }
}
```

## 4. Pick the right endpoint

Rank the live web for a query.

Pull clean markdown from specific URLs.

Multi-step research with a citation basis - sync or async.

An OpenAI-compatible chat endpoint, web-grounded.

Enumerate entities matching a description.

Track a query on a schedule with webhooks.