Navigation
Higress AI-Search MCP Server: Real-Time Precision & Speed - MCP Implementation

Higress AI-Search MCP Server: Real-Time Precision & Speed

Higress AI-Search MCP Server boosts AI responses with real-time data integration, ensuring precision and speed for seamless decision-making.

Visit Repository
✨ Research And Data
4.2(65 reviews)
97 saves
45 comments

Ranked in the top 5% of all AI tools in its category

About Higress AI-Search MCP Server

What is Higress AI-Search MCP Server: Real-Time Precision & Speed?

Higress AI-Search MCP Server is a specialized service designed to enhance AI model responses by integrating real-time data from multiple search engines and internal knowledge bases. It leverages Higress's ai-search framework to deliver precise, up-to-date information, ensuring models can access contextual data dynamically. The solution prioritizes low-latency performance while maintaining scalability for enterprise-level workloads.

How to Use Higress AI-Search MCP Server: Real-Time Precision & Speed?

Implementation requires two core steps: configuring the environment and deploying the service. First, install the required dependencies including the uv runtime and Higress platform components. Next, configure environment variables such as AISERVICE_API_KEY and select preferred search providers. Deployment supports two modes: UVX for simplified package management or UV for granular control over dependencies. Full instructions include API endpoint setup and authentication workflows.

Higress AI-Search MCP Server Features

Key Features of Higress AI-Search MCP Server: Real-Time Precision & Speed?

  • Multi-source integration: Supports web-scale search engines (e.g., Google, Bing) and enterprise knowledge graphs
  • Context-aware filtering: Automatically prioritizes results based on query type and user permissions
  • Latency optimization: Dedicated caching layer reduces response times by 40% in benchmark tests
  • Compliance-ready: Built-in data sanitization and access controls for regulatory environments

Use Cases of Higress AI-Search MCP Server: Real-Time Precision & Speed?

Common applications include:

  • Enterprise Knowledge Platforms: Instant access to internal documents and policy databases
  • Customer Support Bots: Real-time web search integration for troubleshooting
  • Academic Research Tools: Cross-referencing peer-reviewed journals and institutional repositories
  • Dynamic Content Generation: Context-aware content creation using verified data sources

Higress AI-Search MCP Server FAQ

FAQ from Higress AI-Search MCP Server: Real-Time Precision & Speed?

Q: How does caching work?

A: Results are cached per-query with TTL based on source volatility. Sensitive data is stored in encrypted ephemeral storage.

Q: Can I add custom search providers?

A: Yes, through the custom_providers.yaml configuration file using Higress's standardized API schema.

Q: What models are compatible?

A: Works with all LLMs supporting the Higress API standard, including custom enterprise models via adapter modules.

Content

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.

Related MCP Servers & Clients

higress

Quick Navigation

MCP Categories

Research And Data