Search MCP Server

Overview

The Search MCP Server is a lightweight, protocol‑compliant service that lets AI assistants and developers discover other Model Context Protocol (MCP) servers directly from the official MCP repository on GitHub. By providing a searchable index of available servers, it eliminates the need for manual browsing or hard‑coding URLs into client applications. This capability is especially valuable in large, distributed AI ecosystems where new servers are frequently added and categorized.

At its core, the server scrapes the official MCP servers list at https://github.com/modelcontextprotocol/servers in real time, caching the results for a configurable period (default six hours). This dynamic data source ensures that clients always receive up‑to‑date information about server names, descriptions, and categories without requiring a full repository clone. The caching layer keeps the service fast while still reflecting recent changes in the MCP ecosystem.

Key features include:

• Search API: The tool allows clients to query servers by name, description, or tag. This is ideal for assistants that need to propose relevant services based on user intent.
• Category Retrieval: The tool exposes a list of all available categories, enabling clients to present organized options or filter results.
• Resource Endpoints: Two MCP resources, and , provide direct access to the full server list or category index, respectively. These can be consumed by any MCP‑compatible client without invoking a tool.
• Dual Mode Operation: The server can run in standard input/output mode for tightly coupled clients or expose an HTTP/SSE endpoint () for broader network access, making it flexible for both local development and production deployments.

In practice, developers can integrate the Search MCP Server into their AI workflows to automate discovery of complementary services. For example, a Claude assistant could search for an image‑generation MCP server when a user requests visual content, then dynamically invoke that server’s tools. Similarly, an IDE plugin could query the server to populate a dropdown of available MCP services for a project. The searchable, cached interface removes friction in connecting AI assistants to the ever‑growing MCP ecosystem, ensuring that developers can focus on building logic rather than managing service discovery.