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

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="
}

Response Format

All tools return responses in the following format:

{
  "errCode": 0,
  "exception": "",
  "msgParams": null,
  "promptMsg": null,
  "success": true,
  "data": {
  }
}

Visual Studio Code Cline Sample

vsCode install cline plugin
mcp server config
use
1. initial model
2. transfer: what is the max age

Contact：

https://discord.gg/mFa3yeq9 Datafocus

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