Higress AI-Search MCP Server

Higress AI-Search MCP Server

A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.

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Tools

ai_search

Enhance AI model responses with real-time search results from search engines. This tool sends a query to Higress, which integrates with various search engines to provide up-to-date information: 🌐 **Internet Search**: Google, Bing, Quark - for general web information 📖 **Academic Search**: Arxiv - for scientific papers and research 👨‍💻 **Internal Knowledge Search**: Company policies, Product documentation, Technical specifications Args: query: The user's question or search query Returns: The enhanced AI response with search results incorporated

README

Higress AI-Search MCP Server

Overview

A Model Context Protocol (MCP) server that provides an AI search tool to enhance AI model responses with real-time search results from various search engines through Higress ai-search feature.

<a href="https://glama.ai/mcp/servers/gk0xde4wbp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/gk0xde4wbp/badge" alt="Higress AI-Search Server MCP server" /> </a>

Demo

Cline

https://github.com/user-attachments/assets/60a06d99-a46c-40fc-b156-793e395542bb

Claude Desktop

https://github.com/user-attachments/assets/5c9e639f-c21c-4738-ad71-1a88cc0bcb46

Features

  • Internet Search: Google, Bing, Quark - for general web information
  • Academic Search: Arxiv - for scientific papers and research
  • Internal Knowledge Search

Prerequisites

Configuration

The server can be configured using environment variables:

  • HIGRESS_URL(optional): URL for the Higress service (default: http://localhost:8080/v1/chat/completions).
  • MODEL(required): LLM model to use for generating responses.
  • INTERNAL_KNOWLEDGE_BASES(optional): Description of internal knowledge bases.

Option 1: Using uvx

Using uvx will automatically install the package from PyPI, no need to clone the repository locally.

{
  "mcpServers": {
    "higress-ai-search-mcp-server": {
      "command": "uvx",
      "args": [
        "higress-ai-search-mcp-server"
      ],
      "env": {
        "HIGRESS_URL": "http://localhost:8080/v1/chat/completions",
        "MODEL": "qwen-turbo",
        "INTERNAL_KNOWLEDGE_BASES": "Employee handbook, company policies, internal process documents"
      }
    }
  }
}

Option 2: Using uv with local development

Using uv requires cloning the repository locally and specifying the path to the source code.

{
  "mcpServers": {
    "higress-ai-search-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "path/to/src/higress-ai-search-mcp-server",
        "run",
        "higress-ai-search-mcp-server"
      ],
      "env": {
        "HIGRESS_URL": "http://localhost:8080/v1/chat/completions",
        "MODEL": "qwen-turbo",
        "INTERNAL_KNOWLEDGE_BASES": "Employee handbook, company policies, internal process documents"
      }
    }
  }
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

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