自然语言分析服务器

git clone https://github.com/Lumenore-Platform/lumenore-mcp.git
cd lumenore-mcp
pip install -r requirements.txt
cp .env.example .env
# Edit .env with your client credentials (LUMENORE_CLIENT_ID and LUMENORE_SECRET)
python main.py

# Example: Get sales insights
result = await client.call_tool("nlq_to_data", {
    "userQuery": "Top 5 sales regions last month",
    "schemaId": 123
})

git clone https://github.com/Lumenore-Platform/lumenore-mcp.git
cd lumenore-mcp
pip install -r requirements.txt

cp .env.example .env

LUMENORE_CLIENT_ID="your_client_id_here"
LUMENORE_SECRET="your_secret_here"
SERVER_URL="https://preview.lumenore.com"

python main.py

from mcp.client import MCPClient

client = MCPClient("http://localhost:8080/mcp", transport="streamable_http")
tools = await client.list_tools()
result = await client.call_tool("nlq_to_data", {
    "userQuery": "Top 5 sales regions last month",
    "schemaId": 123
})

curl -I http://localhost:8080  # Should return HTTP/1.1 200 OK

{
  "userQuery": "Top 5 sales regions last month",
  "schemaId": 123
}

# Step 1: Get available datasets
metadata = await client.call_tool("get_dataset_metadata", {})

# Step 2: Query sales data
sales_data = await client.call_tool("nlq_to_data", {
    "userQuery": "Show me total sales by region for Q4 2024, sorted by highest sales",
    "schemaId": 35403
})

# Step 3: Analyze trends
trend_analysis = await client.call_tool("get_trend_data", {
    "userQuery": "What are the monthly sales trends for Q4 2024?",
    "schemaId": 35403
})

# Step 4: Identify top performers
pareto_analysis = await client.call_tool("get_pareto_data", {
    "userQuery": "Which products contribute to 80% of our sales?",
    "schemaId": 35403
})

# Step 1: Get customer dataset information
metadata = await client.call_tool("get_dataset_metadata", {})

# Step 2: Analyze customer segments
customer_data = await client.call_tool("nlq_to_data", {
    "userQuery": "Show customer demographics and average order value by age group",
    "schemaId": 35404
})

# Step 3: Detect unusual patterns
anomaly_detection = await client.call_tool("get_outlier_data", {
    "userQuery": "Find customers with unusually high order values or frequency",
    "schemaId": 35404
})

# Step 4: Understand correlations
correlation_analysis = await client.call_tool("get_correlation_data", {
    "userQuery": "What factors correlate with customer lifetime value?",
    "schemaId": 35404
})

# Step 1: Get inventory dataset
metadata = await client.call_tool("get_dataset_metadata", {})

# Step 2: Analyze historical demand
demand_data = await client.call_tool("nlq_to_data", {
    "userQuery": "Show monthly demand for each product category over the past year",
    "schemaId": 35405
})

# Step 3: Identify trends
trend_analysis = await client.call_tool("get_trend_data", {
    "userQuery": "What are the demand trends for each product category?",
    "schemaId": 35405
})

# Step 4: Generate forecasts
forecast = await client.call_tool("get_prediction_data", {
    "userQuery": "Predict demand for next 3 months by product category",
    "schemaId": 35405
})

# Step 5: Detect demand changes
change_detection = await client.call_tool("get_change_data", {
    "userQuery": "Have there been any significant changes in demand patterns recently?",
    "schemaId": 35405
})

# Validation errors
{
    "error": "Invalid request parameters: <details>",
    "status": "validation_error",
    "query": "<user_query>",
    "schema_id": <schema_id>
}

# Service errors
{
    "error": "<Operation> failed: <details>",
    "status": "error",
    "query": "<user_query>",
    "schema_id": <schema_id>
}

# Example: Testing with curl (adjust based on MCP protocol)
curl -N http://localhost:8080

User Query → MCP Server → Lumenore Backend → Results → User
             (No storage)   (Lumenore's privacy policy applies)

# Secure credentials management
export LUMENORE_CLIENT_ID="your_client_id"
export LUMENORE_SECRET="your_secret"

# Restrict file permissions
chmod 600 .env
chmod 700 /path/to/server/directory

# Use secure network connections only
# Avoid using public Wi-Fi for sensitive operations

{
    "server": {
        "lumenore-server": {
            "type": "streamable-http",
            "url": "http://localhost:8080/mcp",
        }
    }
}

from config import config
from core.lumenore_analytics import LumenoreAnalytics
import asyncio

async def test():
    client = LumenoreAnalytics()
    result = await client.make_request('get-domain', method='GET')
    print(result)

asyncio.run(test())

# Clone your fork
git clone <your-fork-url>
cd lumenore-mcp

# Install development dependencies
pip install -r requirements.txt

# Run tests (if available)
python -m pytest