Web Search API for Autonomous Agents

The Problem

When building an autonomous agent using frameworks like LangChain, AutoGen, or raw LLM APIs, developers often equip their agents with a generic web search tool (e.g., Google Search API, SerpAPI, or Tavily).

However, these search engines are optimized for human readers, not agents. When an agent searches for a capability (e.g., "flight booking API" or "python PDF library"), traditional search engines return a mix of:

• Listicles ("Top 10 Flight APIs")
• SEO-optimized blog posts
• News articles
• Reddit threads

The agent must fetch and read these pages, wasting thousands of tokens, only to discover there is no API endpoint it can actually call.

The Solution: Action-First Ranking

Query Here solves this by acting as a discovery layer optimized specifically for agents. When your agent queries the `POST /v1/search` endpoint, we use a hybrid search engine that combines our curated registry with live web candidates.

More importantly, our search model applies an Actionability Score. We actively de-rank articles, reviews, and news in favor of direct provider pages, OpenAPI specs, GitHub repos, and MCP servers.

How It Works

1. The Query: Your agent sends a natural language query: "find an API to convert HTML to PDF".
2. The Search: Query Here searches the web, filtering out blog posts.
3. The Response: We return a JSON array of actionable candidates, complete with access_modes (e.g., api, cli, sdk).
4. The Next Step: Your agent uses the returned documentation_url or OpenAPI spec to instantly write code and execute the task.