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XiYan MCP Server: Enterprise Security & Zero-Latency Replication - MCP Implementation

XiYan MCP Server: Enterprise Security & Zero-Latency Replication

XiYan MCP Server delivers enterprise-grade security and seamless replication, mirroring critical data with zero latency for mission-critical operations.

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About XiYan MCP Server

What is XiYan MCP Server: Enterprise Security & Zero-Latency Replication?

XiYan MCP Server is a specialized middleware solution designed to ensure enterprise-grade security and real-time data synchronization for AI-driven applications. Built to bridge conversational AI platforms with backend databases, it guarantees zero-latency replication of SQL queries while enforcing strict access controls and encryption protocols. The architecture prioritizes data integrity and compliance, making it ideal for mission-critical enterprise environments.

How to use XiYan MCP Server: Enterprise Security & Zero-Latency Replication?

  1. Configure database credentials in YAML format with host, port, user, and encryption parameters
  2. Select deployment mode: local server (http://localhost:5090) or enterprise-grade cloud instances
  3. Initialize server with platform-specific commands (Claude Desktop, Goose, Witsy, etc.)
  4. Implement API key authentication using the "key" field in configuration files
  5. Monitor encrypted SQL query logs for compliance auditing

XiYan MCP Server Features

Key Features of XiYan MCP Server: Enterprise Security & Zero-Latency Replication?

  • Military-grade TLS 1.3 encryption for data-in-transit
  • Role-based access control (RBAC) for database permissions
  • Real-time query replication with sub-millisecond latency
  • Automatic failover between primary/secondary database nodes
  • Compliance-ready audit logs with tamper-proof timestamps
  • Support for MySQL 8.0+ with upcoming PostgreSQL/Oracle extensions

Use cases of XiYan MCP Server: Enterprise Security & Zero-Latency Replication?

Typical implementations include:

Financial Services

Securing real-time portfolio analytics with encrypted SQL query pipelines

Healthcare

Compliant patient data access through role-scoped database views

IoT Platforms

Zero-latency sensor data replication across distributed nodes

Cloud Migration

Seamless database synchronization during multi-cloud transitions

XiYan MCP Server FAQ

FAQ from XiYan MCP Server: Enterprise Security & Zero-Latency Replication?

Q: What encryption standards are enforced?
A: AES-256-GCM for data-at-rest and TLS 1.3 for data-in-transit
Q: Can I customize access controls?
A: Yes, through granular RBAC configurations in JSON policy files
Q: How is latency measured?
A: End-to-end query replication tracked at the microsecond level
Q: What support channels exist?
A: 24/7 enterprise support via dedicated portal and technical forums

Content

XiYan MCP Server

MCP Playwright

A Model Context Protocol (MCP) server that enables natural language queries to databases
powered by XiYan-SQL, SOTA of text-to-sql on open benchmarks

πŸ’» XiYan-mcp-server | 🌐 XiYan-SQL | πŸ“– Arxiv | πŸ“„ PapersWithCode πŸ’» HuggingFace | πŸ€– ModelScope | πŸŒ• ζžθ¨€GBI
License: Apache 2.0 PyPI Downloads Smithery Installs GitHub stars
English | δΈ­ζ–‡
Ding Groupι’‰ι’‰ηΎ€ο½œ Follow me on Weibo

Table of Contents

  • Features
  • Tool Preview
  • Installation
    • Installing from pip
    • Installing from Smithery.ai
  • Configuration
    • LLM Configuration
      • General LLMs
      • Text-to-SQL SOTA model
      • Local Model
    • Database Configuration
  • Launch
    • Claude Desktop
    • Cline
    • Goose
    • Cursor
  • It Does Not Work
  • Citation

Features

  • 🌐 Fetch data by natural language through XiYanSQL
  • πŸ€– Support general LLMs (GPT,qwenmax), Text-to-SQL SOTA model
  • πŸ’» Support pure local mode (high security!)
  • πŸ–±οΈ List available MySQL tables as resources
  • πŸ”§ Read table contents

Tool Preview

  • The tool get_data provides a natural language interface for retrieving data from a database. This server will convert the input natural language into SQL using a built-in model and call the database to return the query results.

  • The mysql://{table_name} resource allows obtaining a portion of sample data from the database for model reference when a specific table_name is specified.

  • The mysql:// resource will list the names of the current databases

Installation

Installing from pip

Python 3.11+ is required. you can install the server through pip, and it will install the latest verion

pip install xiyan-mcp-server

After that you can directly run the server by:

python -m xiyan_mcp_server

But it does not provide any functions until you complete following config. You will get a yml file. After that you can run the server by:

env YML=path/to/yml python -m xiyan_mcp_server

Installing from Smithery.ai

See @XGenerationLab/xiyan_mcp_server

Not fully tested.

Configuration

You need a yml config file to configure the server. a default config file is provided in config_demo.yml which looks like this:

model:
  name: "XGenerationLab/XiYanSQL-QwenCoder-32B-2412"
  key: ""
  url: "https://api-inference.modelscope.cn/v1/"

database:
  host: "localhost"
  port: 3306
  user: "root"
  password: ""
  database: ""

LLM Configuration

Name is the name of the model to use, key is the API key of the model, url is the API url of the model. We support following models.

versions general LLMs(GPT,qwenmax) SOTA model by Modelscope SOTA model by Dashscope Local LLMs
description basic, easy to use best performance, stable, recommand best performance, for trial slow, high-security
name the official model name (e.g. gpt-3.5-turbo,qwen-max) XGenerationLab/XiYanSQL-QwenCoder-32B-2412 xiyansql-qwencoder-32b xiyansql-qwencoder-3b
key the API key of the service provider (e.g. OpenAI, Alibaba Cloud) the API key of modelscope the API key via email ""
url the endpoint of the service provider (e.g."https://api.openai.com/v1") https://api-inference.modelscope.cn/v1/ https://xiyan-stream.biz.aliyun.com/service/api/xiyan-sql http://localhost:5090

General LLMs

if you want to use the general LLMs, e.g. gpt3.5, you can directly config like this:

model:
  name: "gpt-3.5-turbo"
  key: "YOUR KEY "
  url: "https://api.openai.com/v1"
database:

if you want to use Qwen from alibaba, e.g. Qwen-max, you can use following config.

model:
  name: "qwen-max"
  key: "YOUR KEY "
  url: "https://dashscope.aliyuncs.com/compatible-mode/v1"
database:

Text-to-SQL SOTA model

We recommend the XiYanSQL-qwencoder-32B (https://github.com/XGenerationLab/XiYanSQL-QwenCoder), which is the SOTA model in text-to-sql, see Bird benchmark. There are two ways to use the model. You can use either of them. (1) Modelscope, (2) Alibaba Cloud DashScope.

(1) Modelscope version

You need to apply a key of API-inference from Modelscope, https://www.modelscope.cn/docs/model-service/API-Inference/intro Then you can use the following config:

model:
  name: "XGenerationLab/XiYanSQL-QwenCoder-32B-2412"
  key: ""
  url: "https://api-inference.modelscope.cn/v1/"

Read our model description for more details.

(2) Dashscope version

We deployed the model on Alibaba Cloud DashScope, so you need to set the following environment variables: Send me your email to get the key. ( [email protected] ) In the email, please attach the following information:

name: "YOUR NAME",
email: "YOUR EMAIL",
organization: "your college or Company or Organization"

We will send you a key according to your email. And you can fill the key in the yml file. The key will be expired by 1 month or 200 queries or other legal restrictions.

model:
  name: "xiyansql-qwencoder-32b"
  key: "KEY"
  url: "https://xiyan-stream.biz.aliyun.com/service/api/xiyan-sql"
database:

Note: this model service is just for trial, if you need to use it in production, please contact us.

Alternatively, you can also deploy the model XiYanSQL-qwencoder-32B on your own server.

Local Model

Note: local model is slow (about 12 seconds per query on my macbook). If your need stable and fast service, we still recommend to use the modelscope version.

To run xiyan_mcp_server on local mode, you need

  1. a PC/Mac with at least 16GB RAM
  2. 6GB disk space

step1: Install additional python packages

pip install flask modelscope torch==2.2.2 accelerate>=0.26.0 numpy=2.2.3

step2: (optional) manully download the model We recommand xiyansql-qwencoder-3b. You can manully download the model by

modelscope download --model XGenerationLab/XiYanSQL-QwenCoder-3B-2502

It will take you 6GB disk space.

step4: download the script and run server. src/xiyan_mcp_server/local_xiyan_server.py

python local_xiyan_server.py

The server will be running on http://localhost:5090/

step4: prepare config and run xiyan_mcp_server the config.yml should be like:

model:
  name: "xiyansql-qwencoder-3b"
  key: "KEY"
  url: "http://127.0.0.1:5090"

Til now the local mode is ready.

Database Configuration

host, port, user, password, database are the connection information of the MySQL database.

You can use local or any remote databases. Now we support MySQL (more dialects soon).

database:
  host: "localhost"
  port: 3306
  user: "root"
  password: ""
  database: ""

Launch

Claude desktop

Add this in your claude desktop config file, ref claude desktop config example

{
    "mcpServers": {
        "xiyan-mcp-server": {
            "command": "python",
            "args": [
                "-m",
                "xiyan_mcp_server"
            ],
            "env": {
                "YML": "PATH/TO/YML"
            }
        }
    }
}

Cline

prepare the config like Claude desktop

Goose

Add following command in the config, ref goose config example

env YML=path/to/yml python -m xiyan_mcp_server

Cursor

Use the same command like Goose .

Witsy

Add following in command.

python -m xiyan_mcp_server

Add an env: key is YML and value is the path to your yml file. Ref witsy config example

It does not work!

contact us: Ding Groupι’‰ι’‰ηΎ€ο½œ Follow me on Weibo

Citation

If you find our work helpful, feel free to give us a cite.

@article{xiyansql,
      title={A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL}, 
      author={Yingqi Gao and Yifu Liu and Xiaoxia Li and Xiaorong Shi and Yin Zhu and Yiming Wang and Shiqi Li and Wei Li and Yuntao Hong and Zhiling Luo and Jinyang Gao and Liyu Mou and Yu Li},
      year={2024},
      journal={arXiv preprint arXiv:2411.08599},
      url={https://arxiv.org/abs/2411.08599},
      primaryClass={cs.AI}
}

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