MCP Kagi Search

MCP Kagi Search bridges the gap between conversational AI assistants and real‑time web search by exposing the powerful Kagi search engine as a first‑class tool within an MCP server. Developers can now ask their AI assistants to fetch up‑to‑date information, verify facts, or surface niche content that would otherwise require manual browsing. This capability is especially valuable in scenarios where static knowledge bases are insufficient, such as research assistants, content generation pipelines, or any application that demands current data without exposing the underlying API keys to end users.

What It Does

The server registers a single tool, , that accepts a natural‑language query and returns structured search results. Under the hood, it forwards the request to Kagi’s API using a secure token stored in an environment variable. The response is parsed into a consistent JSON format, making it trivial for downstream AI components to consume or display the results. Because MCP handles authentication and request routing, developers can focus on building higher‑level logic rather than managing HTTP sessions or error handling.

Key Features

• Secure API Token Management – The server reads the Kagi token from an environment variable, keeping credentials out of code and version control.
• Structured Output – Results are returned as JSON, enabling easy extraction of titles, URLs, snippets, and relevance scores.
• Simple Tool Signature – The tool accepts a single string argument, allowing AI assistants to call it with natural language queries without needing additional parameters.
• Extensible Architecture – Built on the MCP framework, the server can be extended with custom prompts or additional tools in future iterations.

Use Cases

Integration with AI Workflows

Within an MCP‑enabled application, the server is added to the tool registry during startup. The AI assistant can then invoke as part of its reasoning loop:

1. Prompt: “Find the latest information on Steve Jobs.”
2. Tool Call:
3. Response Handling: The assistant parses the JSON, extracts relevant snippets, and incorporates them into its final answer.

Because MCP handles serialization automatically, developers can skip boilerplate code for HTTP requests and focus on orchestrating the tool calls within their conversational logic.

Standout Advantages

• Zero Boilerplate – No need to write custom HTTP clients or error handlers; MCP’s tooling does it for you.
• Security‑First Design – Tokens are never exposed to the client, reducing the risk of credential leakage.
• Rapid Prototyping – The server can be spun up locally in minutes, making it ideal for experimentation and proof‑of‑concepts.

MCP Kagi Search transforms a commercial search API into an AI‑friendly tool, empowering developers to build smarter, more up‑to‑date assistants with minimal effort.