Database · MODEL CONTEXT PROTOCOL

FocusSearch focus mcp sql

Name: FocusSearch focus mcp sql
Author: FocusSearch

A NL2SQL plugin based on FocusSearch keyword parsing, offering greater accuracy, higher speed, and more reliability!

Open source All MCP Servers

WHAT IT CONNECTS

What FocusSearch focus mcp sql gives an AI Agent

jdk 23 or higher. Download [jdk](https://www.oracle.com/java/technologies/downloads/)
gradle 8.12 or higher. Download [gradle](https://gradle.org/install/)
register [Datafocus](https://www.datafocus.ai/) to obtain bearer token:
Register an account in [Datafocus](https://www.datafocus.ai/)
Create an application
Enter the application
Admin -> Interface authentication -> Bearer Token -> New Bearer Token
Clone this repository:
Build the server:
`model` (required): table model
`bearer` (required): bearer token
`language` (optional): language ['english','chinese']
`chatId` (required): chat id
`input` (required): Natural language
`bearer` (required): bearer token
vsCode install cline plugin
mcp server config
use
initial model
transfer: what is the max age

SECURITY

An MCP listing is not a security review

An MCP server may receive model context, credentials, local files, or permission to call external systems. Review its code, requested environment variables, network behavior, package provenance, and maintenance status before connecting it to an agent.

README

Repository documentation

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

View README on GitHub

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