Why Lovrabet MCP?
The Secret to 10x Efficiency Improvement
When you use AI editors like Cursor, Claude Code to write code, what's your biggest pain point?
AI knows nothing about your business.
It doesn't know:
- What data tables you have? What are the field types?
- What's the relationship between orders and customers?
- Which fields are enum values, what are the optional values?
- Is this field required or optional?
- What's the business meaning behind the data?
The result is:
- AI can only guess, generating code full of hallucinations
- You need to repeatedly modify and debug
- Low development efficiency, frequent bugs
Our Solution
Imagine if you could give AI a complete business model map:
customer table
├── Basic info: id, name, phone, email
├── Enum fields: status (0=disabled, 1=enabled, 2=locked)
├── Primary key: id
└── Related table: order (via customer_id)
order table
├── Basic info: id, order_no, amount
├── Status flow: pending → paid → shipped → completed
└── Required fields: customer_id, product_id
AI is like having a clear map, knowing each field:
- Type: NUMBER, STRING, BOOLEAN
- Constraints: required/optional, primary key/foreign key
- Business meaning: what this field represents in business
- Enum values: what are the optional values for status
The result?
Code generated in one shot, accurate and error-free.
Our Core Technology: Database Reverse Engineering
Our pioneering database reverse engineering technology directly reverses the complete business model from the Lovrabet platform:
- Automatic dataset discovery - Automatically identifies all available data tables
- Field semantic analysis - Analyzes field types, constraints, default values
- Business relationship inference - Infers business relationships between tables
- Enum value extraction - Extracts optional values for enum fields
- API operation mapping - Automatically maps to SDK operations
This is why Lovrabet MCP enables AI to write code with god-like perspective.
Comparison: With MCP vs Without MCP
Scenario 1: Basic Fields and Types
Without Lovrabet MCP:
AI: Help me create a customer list page
AI (guesses): Assuming customer table has these fields:
- customerName (string)
- customerPhone (string)
- isActive (boolean)
You (check): ❌ Actually it's `customer_name`, `phone`, `status`
status is enum value (0/1/2), not boolean
With Lovrabet MCP:
AI: Help me create a customer list page
MCP provides complete info:
- Fields: customer_name, phone, status
- Types: STRING, STRING, NUMBER
- Required: customer_name, phone
- Enum: status (0=disabled, 1=enabled, 2=locked)
AI (god's perspective): Completely accurate, one-time generation, no debugging needed
Scenario 2: ToB Multi-Table Association Queries
Without Lovrabet MCP:
AI: Help me write an order list showing customer name and product name
AI (guesses):
SELECT o.*, c.name, p.name
FROM orders o
LEFT JOIN customer c ON o.cid = c.id ❌ Wrong field guess
LEFT JOIN products p ON o.pid = p.id ❌ Wrong table guess
You (check): ❌ Actually it's customer_id, product_id
Table names are customer, product, but no FK relationship
With Lovrabet MCP:
AI: Help me write an order list showing customer name and product name
MCP automatically analyzes:
- order table has customer_id, product_id fields
- Intelligently infers relationships through field naming (even without FKs)
- Provides joinableFields: customer_id → customer.id, product_id → product.id
AI (intelligent inference):
SELECT o.order_no, o.amount,
c.customer_name, p.product_name
FROM order o
LEFT JOIN customer c ON o.customer_id = c.id ✅ Accurate
LEFT JOIN product p ON o.product_id = p.id ✅ Accurate
Scenario 3: Automatic High-Precision SQL and Backend Function Writing
Without Lovrabet MCP:
AI: Help me write a SQL to calculate monthly order amounts
AI (generates):
SELECT DATE_FORMAT(created_at, '%Y-%m') as month, SUM(amount)
FROM orders
GROUP BY month
You (debug): ❌ Field name is create_time not created_at
❌ Amount field is total_amount not amount
❌ Table name is order not orders
With Lovrabet MCP:
AI: Help me write a SQL to calculate monthly order amounts
MCP provides:
- Complete order table structure: order_no, total_amount, create_time
- Field types: create_time is DATETIME, total_amount is DECIMAL
- 5-step mandatory workflow: Query → Generate → Validate → Save → Test
AI (one-shot):
SELECT DATE_FORMAT(create_time, '%Y-%m') as month,
SUM(total_amount) as total
FROM `order`
GROUP BY month
ORDER BY month DESC
✅ Accurate field names, correct syntax, auto-validated
Backend Function Automatic Writing:
AI: Help me write a Before script, non-admins can only see their own orders
MCP provides:
- order table fields: created_by, user_id
- Script type spec: Filter interface must operate on ytWhere
- Complete Before/After script templates
AI (auto-generates):
export default async function beforeFilter(params, context) {
if (context.userInfo.role !== "admin") {
params.ytWhere = params.ytWhere || {};
const originalWhere = { ...params.ytWhere };
params.ytWhere = {
$and: [
originalWhere,
{ created_by: { $eq: context.userInfo.id } }
]
};
}
return params;
}
✅ Compliant with spec, ready to use
Scenario 4: SDK Parameter Auto-Configuration
Without Lovrabet MCP:
AI: Help me call SDK to query orders with status "completed"
AI (guesses):
await client.models.order.getList({
status: 'completed', ❌ Status value is enum number 2
sortBy: 'createTime', ❌ Parameter name is orderBy
limit: 20 ❌ Parameter name is pageSize
})
You (debug): ❌ All parameters wrong, need to check docs and debug repeatedly
With Lovrabet MCP:
AI: Help me call SDK to query orders with status "completed"
MCP provides:
- Complete filter operation params: where, select, orderBy, currentPage, pageSize
- Enum values: status (0=pending, 1=in_progress, 2=completed)
- Operator spec: must use $eq, $gte, etc.
AI (standard call):
const result = await client.models.order.filter({
where: { status: { $eq: 2 } },
select: ['id', 'order_no', 'total_amount'],
orderBy: [{ create_time: 'desc' }],
currentPage: 1,
pageSize: 20
});
✅ Accurate parameters, compliant, one-shot success
📊 Comprehensive Comparison Table
| ToB Business Scenario | Without MCP | With Lovrabet MCP |
|---|---|---|
| Field name errors | Guessing, repeated debugging | Direct access, zero errors |
| Enum value errors | Rely on memory, often wrong | Read from definition, 100% accurate |
| Multi-table joins | Guess table/field names | Intelligently infer relationships |
| SQL writing | Syntax/field errors | 5-step workflow, auto-validated |
| Backend Function | Unfamiliar with specs, error-prone | Provide templates, auto-generate |
| SDK calls | Wrong param names and values | Accurate parameter configuration |
| Dev efficiency | 5-10 debug iterations | 1-shot generation success |
Why Lovrabet MCP?
Because Other MCPs Only Provide "Data", We Provide "Understanding"
| Dimension | Other Database MCP | Lovrabet MCP |
|---|---|---|
| Data source | Direct database connection | Reverse analyzed from Lovrabet platform |
| Metadata | Basic field info | Complete business semantics + business rules |
| Relationship discovery | Only through foreign keys | Intelligently infer business relationships |
| Enum values | Rely on database constraints | Extracted from dataset definitions |
| API mapping | Need manual writing | Auto-generate SDK code |
Making AI Think Like a Business Expert
Lovrabet MCP enables AI to not just write code, but think like a business expert:
- Understand business rules - Extracted from dataset definitions
- Follow constraints - Knows which fields are required, which have enum restrictions
- Use correct APIs - Automatically selects correct API operations
- Generate runnable code - Includes complete error handling and type definitions
Final Result
| Without Lovrabet MCP | With Lovrabet MCP |
|---|---|
| AI guesses, code full of hallucinations | AI has complete business context |
| Repeated debugging, 5-10 iterations | One-shot generation, accurate |
| Low efficiency, frequent bugs | 10x efficiency improvement |
Start using Lovrabet MCP and make AI your business expert.
Next → Quick Start