FocusSearch/focus_mcp_sql

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, focusmcpsql 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 focusmcpsql and others:
#### Comparison Analysis Table
Here’s a side-by-side comparison of focusmcpsql with other LLM-based frameworks:
| Feature | Traditional LLM Frameworks | focusmcpsql |
| 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
1. Clone this repository:
bash
git clone https://github.com/FocusSearch/focusmcpsql.git
cd focusmcpsql
2. Build the server:
bash
gradle clean
gradle bootJar
The jar path: build/libs/focusmcpsql.jar
## MCP Configuration
Add the server to your MCP settings file:
json
"mcpServers": {
"focusmcpdata": {
"command": "java",
"args": [
"-jar",
"path/to/focusmcpsql/focusmcpsql.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:
json
"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|