Kagi MCP Server

The Kagi MCP server bridges Claude and other AI assistants with the high‑performance, privacy‑focused search engine that powers Kagi Search. By exposing a lightweight “search” tool and an optional video summarizer, the server solves the problem of unreliable or generic web‑search capabilities that many assistants provide out of the box. Developers can therefore replace a default, often noisy search with Kagi’s precise indexing and ranking algorithm, ensuring that AI responses are grounded in up‑to‑date, high‑quality content.

At its core, the server implements two primary capabilities. First, a search tool accepts natural‑language queries and returns concise, ranked results from Kagi’s indexed web corpus. Second, a summarizer tool can ingest video URLs and produce text summaries using a selectable engine (defaulting to “cecil”). These tools are exposed through the MCP interface, allowing a client to invoke them as if they were native functions. Because the server handles authentication via an API key, developers can secure access and monitor usage without exposing credentials in client code.

Key features include:

• Closed‑beta API access that guarantees high relevance and speed, with the option to enable or disable it per environment.
• Engine selection for summarization, giving developers control over the trade‑off between speed and depth.
• Seamless integration with Claude Desktop through Smithery, simplifying deployment for end users.
• Debugging hooks via the MCP inspector, which provide visibility into tool invocation and response handling.

Real‑world use cases span content creation, research assistants, and knowledge bases. A marketing team can ask the assistant to “find recent studies on AI ethics” and receive authoritative links instantly. A video editor might request a summary of a long webinar, allowing the assistant to surface key points without manual transcription. In academic settings, students can query for recent publications on a niche topic and trust that the returned URLs are relevant and up‑to‑date.

Integrating Kagi MCP into an AI workflow is straightforward: the server registers itself with the client, and the assistant can invoke the search or summarizer tools via standard MCP calls. Because the server handles all network communication and result parsing, developers can focus on higher‑level logic—such as chaining search results into further analysis or feeding summaries into downstream models. The combination of precise search, optional summarization, and tight integration makes Kagi MCP a standout choice for developers seeking reliable web knowledge within their AI applications.