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Higress AI-Search MCP Server

Higress AI-Search MCP Server

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github.com/cr7258Search5Updated 77w ago
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About Higress AI-Search MCP Server

Higress AI-Search MCP Server provides real-time web, academic, and internal knowledge search through Higress ai-search. It is intended for AI clients that need search results to inform responses.

Key features

  1. 01Search the web with Google, Bing, and Quark
  2. 02Search academic papers on Arxiv
  3. 03Search internal knowledge

Use cases

  1. 01Find current information from the web
  2. 02Look up scientific papers and research
  3. 03Search configured internal knowledge bases

MCP support

It exposes an AI search tool to MCP clients, routing searches through Higress ai-search for web, academic, and internal knowledge results.

#higress#ai-search#mcp-server

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "higress-ai-search": {
      "command": "uvx",
      "args": [
        "higress-ai-search-mcp-server"
      ],
      "env": {
        "HIGRESS_URL": "<HIGRESS_URL>",
        "MODEL": "<MODEL>",
        "INTERNAL_KNOWLEDGE_BASES": "<INTERNAL_KNOWLEDGE_BASES>"
      }
    }
  }
}
Run commanduvx higress-ai-search-mcp-server
Environment variables to set
HIGRESS_URL
MODEL
INTERNAL_KNOWLEDGE_BASES

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypihigress-ai-search-mcp-server

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MseeP.ai Security Assessment Badge

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.

Higress AI-Search Server MCP server

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.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.