XiYan MCP Server

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}

💻 XiYan-mcp-server | 🌐 XiYan-SQL | 📖 Arxiv | 📄 PapersWithCode

English | 中文

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 LLMs
  • Database Configuration
• Launch
  • Claude Desktop
• Citation

Features

• 🌐 Fetch data by natural language through XiYanSQL
• 🖱️ List available MySQL tables as resources
• 🔧 Read table contents

Tool Preview

• The tool get_data_via_natural_language 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

bash
pip install xiyan-mcp-server

After that you can directly run the server by:

bash
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:

yaml
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:

yaml
model:
  name: "pre-xiyan_multi_dialect_v3"
  key: ""
  url: "https://poc-dashscope.aliyuncs.com/compatible-mode/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.

general LLMs

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

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

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

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

Text-to-SQL SOTA model

Last, we recommend the XiYanSQL-qwencoder-32B (https://github.com/XGenerationLab/XiYanSQL-QwenCoder), which is the SOTA model in text-to-sql. We deployed the model on Alibaba Cloud DashScope, so you need to set the following environment variables: Contact us to get the key. ( godot.lzl@alibaba-inc.com )

yaml
model:
  name: "pre-xiyan_multi_dialect_v3"
  key: "KEY"
  url: "https://poc-dashscope.aliyuncs.com/compatible-mode/v1"
database:

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

Local LLMs

To support in the future.

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).

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

Launch

Claude desktop

Add this in your claude desktop config file

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

Citation

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

bib
@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}
}