WebSearch MCP

WebSearch‑MCP is a Model Context Protocol server that equips AI assistants with real‑time web search capability. By acting as an intermediary between a crawler API and MCP‑enabled clients, it allows models such as Claude to query the internet on demand, retrieve up-to-date results, and incorporate them into responses. This solves a common limitation of offline or static knowledge bases: the inability to surface fresh information about breaking news, niche topics, or rapidly evolving domains.

The server exposes a simple MCP endpoint that accepts search queries and forwards them to an underlying WebSearch Crawler API. The crawler, which can be run via Docker Compose with a pre‑built image, scrapes the web and returns structured results. WebSearch‑MCP then packages these results into the standardized MCP response format, ensuring compatibility with any client that understands the protocol. Developers can configure key parameters—such as the crawler API URL and maximum result count—through environment variables, giving them control over latency, cost, and the breadth of information returned.

Key capabilities include:

• Real‑time search: AI assistants can request fresh data at any moment, bypassing the static knowledge cutoff of the model.
• Customizable result limits: Set how many results to return per query, balancing detail against response time.
• Seamless MCP integration: The server speaks the Model Context Protocol natively, so no additional adapters are needed for Claude Desktop, Cursor IDE, or other MCP clients.
• Docker‑ready crawler: The bundled crawler image simplifies deployment; it handles anti‑bot measures via FlareSolverr and persists data in a Docker volume.

Typical use cases span from software developers needing up‑to‑date API documentation, to researchers tracking the latest academic papers, or content creators verifying facts before publishing. In an AI workflow, a user can ask the assistant to “search for recent developments in quantum computing,” and the MCP server will fetch current web pages, parse them, and return concise snippets that the model can weave into a coherent answer. This tight coupling between search and generation eliminates hallucinations about recent events and enhances trustworthiness.

WebSearch‑MCP stands out by offering a turnkey, protocol‑native solution that requires minimal configuration beyond pointing to a crawler service. Its Docker composition makes it trivial to scale or replace the underlying search engine, while its environment‑variable configuration keeps deployment flexible across CI/CD pipelines and local development environments. For developers building AI tools that demand up‑to‑date knowledge, this MCP server provides a reliable bridge between static language models and the dynamic web.