Higress ai-search MCP Server
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# 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](https://higress.cn/) [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) feature. ## Demo [Demo video placeholder] ## Features - **Internet Search**: Google, Bing, Quark - for general web information - **Academic Search**: Arxiv - for scientific papers and research - **Internal Knowledge Search** ## Prerequisites - [uv](https://github.com/astral-sh/uv) for package installation. - Config Higress with [ai-search](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-search/README.md) plugin and [ai-proxy](https://github.com/alibaba/higress/blob/main/plugins/wasm-go/extensions/ai-proxy/README.md) plugin. ## 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. ```bash { "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. ```bash { "mcpServers": { "higress-ai-search-mcp-server": { "command": "uv", …
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