FOCUS DATA MCP Server [中文]

A Model Context Protocol (MCP) server enables artificial intelligence assistants to convert natural language into SQL statements.

There are already so many Text-to-SQL frameworks. Why do we still need another one?

In simple terms, focus_mcp_sql adopts a two-step SQL generation solution, which enables control over the hallucinations of LLM and truly builds the trust of non-technical users in the generated SQL results.

Below is the comparison table between focus_mcp_sql and others:

Comparison Analysis Table

Here’s a side-by-side comparison of focus_mcp_sql with other LLM-based frameworks:

Feature	Traditional LLM Frameworks	focus_mcp_sql
Generation Process	Black box, direct SQL generation	Transparent, two-step (keywords + SQL)
Hallucination Risk	High, depends on model quality	Low, controllable (keyword verification)
Speed	Slow, relies on large model inference	Fast, deterministic keyword-to-SQL
Cost	High, requires advanced models	Low, reduces reliance on large models
Non-Technical User Friendliness	Low, hard to verify results	High, easy keyword checking

Features

-Initialize the model -Convert natural language to SQL statements

Related MCP server: X Tools for Claude MCP

Prerequisites

jdk 23 or higher. Download jdk
gradle 8.12 or higher. Download gradle
register Datafocus to obtain bearer token:
1. Register an account in Datafocus
2. Create an application
3. Enter the application
4. Admin -> Interface authentication -> Bearer Token -> New Bearer Token

Installation

Clone this repository:

git clone https://github.com/FocusSearch/focus_mcp_sql.git cd focus_mcp_sql

Build the server:

gradle clean gradle bootJar The jar path: build/libs/focus_mcp_sql.jar

MCP Configuration

Add the server to your MCP settings file:

{ "mcpServers": { "focus_mcp_data": { "command": "java", "args": [ "-jar", "path/to/focus_mcp_sql/focus_mcp_sql.jar" ], "autoApprove": [ "gptText2sqlStart", "gptText2sqlChat" ] } } }

Available Tools

1. gptText2sqlStart

initial model.

Parameters:

model (required): table model
bearer (required): bearer token
language (optional): language ['english','chinese']

Example:

{ "model": { "tables": [ { "columns": [ { "columnDisplayName": "name", "dataType": "string", "aggregation": "", "columnName": "name" }, { "columnDisplayName": "address", "dataType": "string", "aggregation": "", "columnName": "address" }, { "columnDisplayName": "age", "dataType": "int", "aggregation": "SUM", "columnName": "age" }, { "columnDisplayName": "date", "dataType": "timestamp", "aggregation": "", "columnName": "date" } ], "tableDisplayName": "test", "tableName": "test" } ], "relations": [ ], "type": "mysql", "version": "8.0" }, "bearer": "ZTllYzAzZjM2YzA3NDA0ZGE3ZjguNDJhNDjNGU4NzkyYjY1OTY0YzUxYWU5NmU=" }

model 参数说明：

名称	位置	类型	必选	说明
model	body	object	是	none
» type	body	string	是	数据库类型
» version	body	string	是	数据库版本
» tables	body	[object]	是	表结构列表
»» tableDisplayName	body	string	否	表显示名
»» tableName	body	string	否	表原始名
»» columns	body	[object]	否	表列列表
»»» columnDisplayName	body	string	是	列显示名
»»» columnName	body	string	是	列原始名
»»» dataType	body	string	是	列数据类型
»»» aggregation	body	string	是	列聚合方式
» relations	body	[object]	是	表关联关系列表
»» conditions	body	[object]	否	关联条件
»»» dstColName	body	string	否	dimension 表关联列原始名
»»» srcColName	body	string	否	fact 表关联列原始名
»» dimensionTable	body	string	否	dimension 表原始名
»» factTable	body	string	否	fact 表原始名
»» joinType	body	string	否	关联类型

2. gptText2sqlChat

Convert natural language to SQL.

Parameters:

chatId (required): chat id
input (required): Natural language
bearer (required): bearer token

Example:

{ "chatId": "03975af5de4b4562938a985403f206d4", "input": "what is the max age", "bearer": "ZTllYzAzZjM2YzA3NDA0ZGE3ZjguNDJhNDjNGU4NzkyYjY1OTY0YzUxYWU5NmU=" }