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Crawlora MCP

@Crawlora-org

About Crawlora MCP

Hosted MCP server for structured public web data — 319 tools across search, maps, commerce, social & finance, returning clean JSON.

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "crawlora": {
      "url": "https://mcp.crawlora.net/mcp",
      "transport": "streamable-http",
      "headers": {
        "Authorization": "Bearer ${CRAWLORA_API_KEY}"
      }
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Crawlora MCP?

Crawlora MCP is a hosted Model Context Protocol server that gives AI clients and agents 795 structured public-web-data tools across 68 platform groups — search, maps, e-commerce, social, finance, travel, app stores, media, and reviews — each returning clean, normalized JSON instead of raw HTML. It is designed for developers and AI agents who need structured web data without maintaining scrapers.

How to use Crawlora MCP?

Connect any MCP client to the hosted endpoint (https://mcp.crawlora.net/mcp) using Streamable HTTP and authenticate with your Crawlora API key (free 2,000 credits/month at crawlora.net). Alternatively, run the open-source local server in this repo via npx -y crawlora-mcp (Node 20+) or Docker, which exposes the same 795 tools over stdio. Ready-to-paste configuration files for Claude Code, Claude Desktop, Cursor, Codex CLI, VS Code, and other MCP clients are provided in the repo's examples/ folder.

Key features of Crawlora MCP

  • 795 structured data tools across 68 platform groups.
  • Normalized JSON output per tool — no HTML parsing.
  • No proxies, browsers, or parsers to maintain.
  • Pay-on-success credit model (billed only on 2xx responses).
  • Single API key for every tool behind one MCP endpoint.
  • Works with any MCP client via Streamable HTTP (hosted) or stdio (local).

Use cases of Crawlora MCP

  • Market research: gather finance quotes, e-commerce products, and social trends.
  • Competitive analysis: compare app store reviews, product ratings, and company data.
  • Content research: pull search results, job postings, and video/podcast metadata.
  • Agent workflows: give LLM agents live web lookups without building scrapers.

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