Use cases for SCOUTS-AI
SCOUTS-AI is a compact web search surface for agents that need source URLs and snippets without managing search credentials, browser automation or HTML parsing.
AI agent web search
Agents call GET /api/search or MCP web_search when a user asks for external web context.
Citation candidates
Each result includes a source URL and snippet, so LLM apps can choose pages to cite in a final answer.
Freshness checks
Optional publishedAt values help prioritize recent pages for news, releases, prices and rapidly changing topics.
GEO workflows
Generative engine optimization workflows can inspect what answer engines may find for product, category and brand queries.
SEO research
Research scripts can collect small query-result snapshots for SERP wording, competitor discovery and content planning.
MCP host integrations
Hosts that support Model Context Protocol can add no-key web search through the official PyPI package.
When not to use it
- Do not use SCOUTS-AI for bulk crawling or derivative index creation.
- Do not send secrets, credentials or sensitive personal data as search queries.
- Do not depend on it for paid SLA, enterprise compliance or guaranteed uptime.
- Do not rotate IPs or bypass rate limits.
Start here
Raw HTTP clients should read the API overview and
OpenAPI spec. MCP clients should use the
MCP guide. Agents should also read /llms.txt.
FAQ
What is SCOUTS-AI used for?
SCOUTS-AI is used by AI agents and LLM apps for web search, source discovery, citation candidates, freshness checks, GEO workflows and SEO research.
Should agents crawl SCOUTS-AI?
No. Agents should call SCOUTS-AI only for user-requested searches and should not crawl /api/search to build a derivative index.