Higress AI Search
Provides real-time search capabilities through Higress ai-search, allowing AI models to access current information from web search engines, academic sources, and internal knowledge bases.
Enhances AI model responses with real-time search results from various engines through Higress ai-search, supporting internet, academic, and internal knowledge searches.
What it does
- Search Google, Bing, and Quark for web information
- Query arXiv for academic papers and research
- Search internal knowledge bases
- Enhance AI responses with real-time search results
Best for
About Higress AI Search
Higress AI Search is a community-built MCP server published by cr7258 that provides AI assistants with tools and capabilities via the Model Context Protocol. Enhance AI responses with Higress AI Search—real-time results via leading engines like Bing AI and Microsoft. Supports i It is categorized under search web.
How to install
You can install Higress AI Search in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
License
Higress AI Search is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
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.
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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