Kagi Server MCP Server: Web Search Integration for AI Applications

Overview: What is Kagi Server MCP Server?

Kagi Server is a TypeScript-based MCP (Model Context Protocol) server designed to integrate with the Kagi API, providing web search and other utility tools for Claude Desktop and other AI applications through the Model Context Protocol. This server demonstrates core MCP concepts by allowing developers and end-users to perform searches using Kagi’s beta API directly within their AI applications.

🔧 Core Features & MCP Capabilities

Kagi Server encompasses a range of features aligned with the MCP protocol, enhancing the capabilities of AI applications that utilize it. Below are its key offerings:

Implemented Tools

• kagi_search: This tool performs web searches using Kagi’s API. It accepts a query string and an optional limit as parameters before returning search results from the Kagi API.

Planned Tools (Not Yet Implemented)

• kagi_summarize: Generates summaries of web pages or text.
• kagi_fastgpt: Provides quick responses through Kagi’s FastGPT service.
• kagi_enrich: Fetches enriched news results on specific topics, enhancing the information retrieved.

Each tool is built to integrate seamlessly with the MCP protocol, ensuring that it can be accessed and used by any compatible client application. For instance, when using kagi_search, developers can prompt their AI applications to perform searches and then review or act upon the results.

⚙️ MCP Architecture & Protocol Implementation

The architecture of Kagi Server is designed around simplicity and ease of integration with the Model Context Protocol. The server uses TypeScript for its implementation, which allows for robust type definitions and smooth compatibility checks. Here’s a breakdown of how it works:

• Dependencies are managed via npm: Installation and configuration are straightforward, making it easy to include in larger systems or development environments.
• Auto-rebuild capabilities during development: Ensures that any changes made to the server code are immediately available for testing.

The MCP protocol flow can be visualized as follows:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This diagram illustrates the flow of communication between an AI application, its respective MCP client, the Kagi Server (as an MCP server), and finally to Kagi’s data sources or tools.

🚀 Getting Started with Installation

Getting started with Kagi Server is relatively simple. The following steps outline the process of setting up the environment:

1. Install dependencies:

   npm install
   

2. Build the server for deployment:

   npm run build
   

3. Utilize auto-rebuild capabilities during development:

   npm run watch
   

For full integration, particularly with tools like Claude Desktop, additional setup is required to define the MCP server configuration.

💡 Key Use Cases in AI Workflows

Kagi Server offers significant benefits for integrating web search functionalities into AI workflows. Consider these practical use cases:

Case Study 1: Research and Documentation Integration

In a research scenario, an academic might need to find the latest advancements in quantum computing. Using Kagi Server MCP server integrated with Claude Desktop, the user can quickly formulate a query and get comprehensive results directly within their application. The kagi_search tool would handle this task by fetching relevant web pages from Kagi’s API.

Case Study 2: Content Generation Assistance

During content creation or academic writing, users often need quick insights into complex topics. With the ability to summarize text using kagi_summarize, an author can input a large document and receive concise summaries that highlight key points. This tool would help streamline the editing process and ensure coherence in the content.

🔌 Integration with MCP Clients

Kagi Server supports seamless integration with various MCP-compatible clients such as Claude Desktop, Continue, and Cursor:

• Claude Desktop: Fully supports kagi_search, enabling users to perform searches directly within their environment.
• Continue: Supports tools like kagi_search for web searches, but lacks certain features currently in development.
• Cursor: Supports only kagi_search as of now; other planned tools are not yet available.

📊 Performance & Compatibility Matrix

The compatibility matrix provides an overview of which MCP clients support which Kagi Server tools:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	❌	Supports basic tools only
Cursor	❌	✅	❌	Limited support

Developers and users should ensure their choices align with the current level of support.

🛠️ Advanced Configuration & Security

To maintain security and functionality, Kagi Server requires initial configuration. This involves setting up an .env file in the root directory to include your API key:

KAGI_API_KEY=your_api_key_here

The environment variable is essential for accessing the API securely.

❓ Frequently Asked Questions (FAQ)

Q1: How do I install Kagi Server as a MCP client?

You can install it via Smithery using:

npx @smithery/cli install kagi-server --client claude

Q2: What tools are currently implemented in the server?

Kagi Search is fully implemented. Other planned tools like kagi_summarize, kagi_fastgpt, and kagi_enrich are yet to be developed.

Q3: Can I run Kagi Server locally for testing purposes?

Yes, you can use:

npm install
npm run build
npm run watch

Q4: How do I ensure the security of my API key?

Store your .env file in a secure location and add it to your .gitignore.

Q5: Are there any performance considerations for this server?

The server is optimized for speed, but large-scale deployments might require additional resource adjustments.

👨‍💻 Development & Contribution Guidelines

Contributing to Kagi Server is straightforward. Contributions are welcome and can be made through Pull Requests (PRs). Potential areas of contribution include:

• Implementing new tools: Add features like kagi_summarize or kagi_fastgpt.
• Improving error handling and input validation.
• Enhancing documentation to make it more comprehensive.

Any contributions should be thoroughly tested before submission.

🌐 MCP Ecosystem & Resources

Kagi Server is part of a broader ecosystem that includes other MCP servers, tools, and libraries designed for integration with various AI applications. For additional resources and information, explore the Model Context Protocol documentation or join the community forums to discuss best practices.

By leveraging Kagi Server, developers can enhance their AI workflows by integrating powerful search functionalities while ensuring seamless interaction with compatible clients through the Model Context Protocol.