WebCat MCP Server

WebCat is a purpose‑built Model Context Protocol (MCP) server that empowers AI assistants with reliable, real‑time web search and content extraction. It solves the common bottleneck of fetching up‑to‑date information by turning any external web source into a first‑class tool that can be called from within an AI workflow. By exposing a single, JSON‑RPC 2.0 endpoint that follows the MCP specification, WebCat lets developers plug it into Claude Desktop, LiteLLM, or any other MCP‑compliant client without modifying the model’s code.

The server delivers two core capabilities. First, it offers a web search tool that can use either the premium Serper API or fall back to DuckDuckGo when a key is not provided. Second, it provides content extraction that pulls the main body of an article or page using Serper’s scrape API for speed and quality, or Trafilatura as a free fallback. Both tools return clean Markdown with the original document structure preserved, making it easy for downstream models to ingest and reason about the retrieved text. The integration is seamless: a single enables both search and scraping, while optional bearer‑token authentication () allows restricted access for production environments.

Key features include automatic provider fallback, parallel request handling for fast throughput, and a lightweight FastMCP implementation that runs in Docker on both Intel/AMD and Apple Silicon platforms. The MCP compliance means the server can be invoked from any client that understands JSON‑RPC over HTTP, and its health/status endpoints provide straightforward monitoring. WebCat’s design prioritizes minimal configuration—environment variables are sufficient to toggle premium services or authentication, and the server’s default port is 8000.

In practice, developers use WebCat to add live web knowledge to conversational agents, data‑collection pipelines, or research assistants. For example, a customer support bot can query the latest product documentation, while an academic assistant can pull recent research articles on demand. Because WebCat abstracts away the complexity of interacting with multiple web APIs, teams can focus on building higher‑level logic and user experiences rather than handling authentication, rate limits, or parsing HTML. The result is a robust, scalable, and easily deployable web‑access layer that extends the reach of AI models into the ever‑changing world of online information.