Chapter 2 : Data MCP Servers

Build Data MCP Servers

• Build MCP Servers with No-code and Customize & Extend to complete your use case.
• Use MCP Servers via AI Editors like Cursor or Cline

Getting Started

Please undertake below tutorial first , if you have not:

• Build Your First App
  • With Focus on Backend Spring-Java

MCP Server Designing

Please consider below aspects when designing MCP Servers:

• AI Tools limit max number of Tools to: 40
• Using data modeling can reduce number of Tools by 4X
• Keep only required Tools from generated code, comment out rest.
• Tool naming is limited to 60 chars. On MCP Server starting, it will give warning and show such offending Tool names. You can rename these Tools in generated code to fit length < 60.

MCP Server - Spring Java

info

• MCP Server - Spring Java is built along with Backend spring-java.
• Project Location in downloaded dir is at e.g.
  • EmGenDir_JohnDoe_WS_51069\WS_51069\mcp\spring-java\emmcp

Run Builds and Explore

tip

MCP Layer Connects to API Layer, REST or GraphQL. So please configure and run destination API Layer first.

• APIs Run Builds and Explore
  • Please execute & run backend/spring-java/emapi project:
  • Compile, Deploy, Run.
  • Refer to Tutorial Section 2-5-A : Backend Spring Java

MCP Server Project Build

• Open Maven Project in IDEA IntelliJ
• Maven Goal: package
• Note down jar location, e.g.:
  • C:\Users\JohnDoe\Downloads\EmGenDir_JohnDoe_WS_51123\WS_51123\mcp\spring-java\emmcp\app\mcprest\target\mcprest-1.0-SNAPSHOT.jar

Configure and Run

Configure MCP Server

• Please follow Readme in downloaded dir:
  • mcp\spring-java\README-MCP-Server.md

Available Tools in Data MCP Server

• To see Available Tools in Data MCP Server for REST and GraphQL,
• Please refer to: Data MCP Server Features

MCP Server For REST

Configure MCP Server For REST

• Please follow Readme in downloaded dir:
  • mcp\spring-java\README-mcprest.md

tip

• Refer to some general screenshots on MCP Server configuring in below also:
  • Tutorial 7 : AI Based Development
    • Chapter 2 : EasyManage Builder MCP Server
      • Cursor

• MCP Added

• MCP Tools

Use MCP Server For REST with Cursor Chat

• Ask Questions and get data insights
• MCP Server For REST Run by following below sections in Readme README-mcprest.md:
  • Available Tools
  • Sample Chats

How to Enable/Disable Tools

NOTE: Some @Tools are commented as prefixed by //, you can enable or disable them inside generated code via global edits easily. Please see section: How to Enable/Disable Tools in README-mcprest.md

tip

You can Disable AI Transactions if the database write operations are not desired.

Example Chat Usage General →

Get API Docs And Schema

easymanage get all api docs

caution

If the api docs return large repsponse, you will get buffer exceeded notice in cursor chat, and AI model might use alternative methods to call individual APIs to get api docs.

get details for entity Product 

get schema for ViewAll

easymanage get all table schema

easymanage get table schema for shipping

Getting Data

General Get Data

easymanage get table data from Product table in table format

You can try input variations:

easymanage get table data from Product table in csv format

easymanage get table data from Product table in excel format

easymanage get table data from Product table in pdf format

tip

• When you ask AI to get data in Excel or PDF format, In such a case, it may,
  • AI model will write custom python script to process and save data returned in desired format.
  • And it can call the generated python script with mcp data response to save it.
  • You will need to have python installed on your computer.

Get Paged Data With Queried & Sorted

• To get queried and sorted data as per parameters as pages
• With Flexible SQL-Like Querying

easymanage get table data as pages from Product table queried as "productId > 2" sorted by "productName desc" with 1 page size 3

• Please note parameters for above Tool call were constructed properly by AI:

• Try some chat queries

easymanage find the top 3 products with maximum inventory quantity

• Once get response then,

save above response to file top-3-products.txt

• file top-3-products.txt will be created.

info

How to Re-use or Share Input Queries & Prompts

You can re-use the input prompts for yourself or other users. Please open a project in Cursor which is mapped to shared drive which stores the shared input prompts files.

e.g.

• Create file product01.md in project root, with contents:

easymanage get table data as pages from Product table queried as "productId > 2" sorted by "productName desc" with 1 page size 3

Then In chat window click @ Add Context and search and select above file as context.

• Then in chat you can enter input:

use prompt from context file

LLM will reply like:

I'll execute the query from the context file to get table data from the Product table with the specified parameters.

and it will provide the response.

AI Transactions

AI Transactions allow AI models to write or update data into database tables via respective API calls.

AI Transactions - Create, Update, Delete

Create

easymanage what tool is available to create customer record

want to see an example of how to use this tool

run the tool with values as below 
{
  "customerId": "4001",
  "customerName": "Customer 4001",
  "created": "2023-03-19 15:53",
  "updated": "2023-03-19 15:53",
  ...
}

• Sample Screen

Update

easymanage what tool is available to update tenant record

want to see an example of how to use this tool

run the tool with values as below
{
  "tenantId" : 2,
  "TenantTblRec1":
  {
  "tenantId" : 2,
  "tenantName" : "New Name Tenant 2",
  ...
  }
} 

• Sample Screen (customer)

Delete

easymanage what tool is available to delete tenant record

want to see an example of how to use this tool

• Sample Screen (customer)

run the tool with value "1010"

• Sample Screen (customer)

Example Chat Usage Ask AI →

See below example chat usages for Ask AI.

info

• Chat examples for Ask AI are run on tables used in Tutorial For Cloud Native Development, please see them here:
  • Cloud Native Development: Sample Tables

Ask AI What Insights and Trends Can Get

easymanage get and study all table schema and suggest what insights and trends can be derived from tables

Begin response:

End response:

On getting chat different responses.

• Please note, if you ask again some time later, you may get varied response, that's normal working of AI tools and models.

e.g. Please see saved 1st ask response as e.g. chat-insights-trends01.md:

Here is sample response:

chat-insights-trends01.md

Analyze table schemas for insights

User

easymanage get and study all table schema and suggest what insights and trends can be derived from tables

---

Cursor

I’m going to fetch the full table schema from the EasyManage service to understand the entities and relationships before proposing insights and trend analyses.

I pulled the full EasyManage table schemas and mapped relationships. Below are high-signal insights and trend ideas you can derive right now, plus gaps to fill for deeper analysis.

Schema overview (key fields)

• Customer: customerId, customerName, startDate
• Tenant: tenantId, customerId, tenantName, beginDate, minOrderQuantity, customizationRequired, custMicrosvcType, customizationService, customizationPayload
• Product: productId, productName, productCategory, primarySupplier
• Orders: orderId, customerId, tenantId, productId, orderDate, orderQuantity, orderStatus, created, updated
• Shipping: shippingId, orderId, customerId, tenantId, productId, shippingDate, shippingQuantity, shippingStatus, created, updated
• Inventory: productId, inventoryDate, inventoryQty
• Joins: OrdersShipping (Orders with array of Shipping), ShippingTenant (Shipping with embedded Tenant)

Core relationships

• Orders ↔ Customer/Tenant/Product via customerId/tenantId/productId
• Shipping ↔ Orders via orderId (also carries customerId/tenantId/productId)
• Inventory snapshots by productId over time
• Supplier attributed at Product level (primarySupplier)

High-value KPIs and how to compute them

• Order volume: count of orderId by day/week/month and by tenantId, productCategory.
• Average order size: avg orderQuantity by period/tenant/product.
• Fulfillment rate: sum(ship shippingQuantity) ÷ sum(order orderQuantity), joined on orderId.
• Order-level fill rate: per order, min(sum(shipQty)/orderQty, 1).
• Lead time: (lead_time_days = min(shippingDate) - orderDate) per orderId.
• On-time ship rate: share of shipments with (shippingDate - orderDate ≤ SLA) (define SLA).
• Split shipments rate: orders with count(distinct shippingId) > 1.
• Cancellation/exception rate: share of orders by orderStatus and shipments by shippingStatus.
• Inventory turnover: sum(shippedQty in period) ÷ avg(inventoryQty) for each productId/category.
• Stockout occurrences: count days with inventoryQty ≤ 0; link to lost sales where orders existed.
• Backorder rate: orders where first shippingDate > orderDate + backorder_threshold.
• Customer growth: new customers by month from Customer.startDate.
• Repeat purchase rate: customers with multiple orderId.
• Tenant benchmarks: compare above KPIs by tenantId; analyze effect of minOrderQuantity.
• Supplier reliability: lead time and fill rate by primarySupplier (via Product join).

Trend and segmentation ideas

• Seasonality: orders, shipments, and lead time by week/month, per productCategory and tenantId.
• Product mix shift: share of orders/shipments by productCategory over time.
• Demand-supply balance: trend of orderQuantity vs inventoryQty for stockout risk.
• Cohorts: customers grouped by startDate month and their order frequency/size over time.
• ABC classification: rank products by shipped volume to focus on A-items for inventory policy.
• Tenant customizations: correlate customizationRequired/custMicrosvcType with lead time, fill rate, and order size.
• Supplier impact: shipping lead time and split shipments by primarySupplier.

Example query patterns (sketch)

• Lead time by tenant:
  • Join Orders to Shipping on orderId; take min(shippingDate) per order; compute days to ship; group by tenantId.
• Fill rate:
  • Sum shippingQuantity per orderId ÷ orderQuantity; aggregate by month/tenant.
• Inventory turnover:
  • Numerator: sum shippingQuantity per product, per period. Denominator: avg inventoryQty over same period.

Data quality checks

• Referential integrity: every Shipping.orderId exists in Orders; matching productId and tenantId.
• Duplicates: no multiple Orders rows with same keys unless intended.
• Temporal sanity: shippingDate ≥ orderDate; updated ≥ created.
• Inventory continuity: non-negative inventoryQty unless backorder is modeled.

Recommended dashboards

• Executive overview: Orders, Shipments, On-time rate, Avg lead time, Fill rate, Inventory turnover.
• Supply chain: Lead time by product/supplier, Split shipments, Backorder/stockout heatmap.
• Merchandising: Category mix, ABC classes, Supplier performance.
• Customer/tenant: New vs repeat orders, Average order size, Tenant benchmarks, effect of minOrderQuantity.

Gaps to consider adding (to deepen insights)

• Price/cost fields for revenue, margin, true inventory turnover by COGS.
• SLA/target delivery fields for precise on-time metrics.
• Return/refund status and quantities.
• Warehouse/location fields for geo and node-level performance.
• Backorder flags and promised dates.

Status: Retrieved all schemas and mapped relationships. Provided concise KPI formulas, trend ideas, and dashboard suggestions tailored to your tables.

• Mapped entities and joins across Orders, Shipping, Inventory, Product, Customer, Tenant
• Listed concrete KPIs with formulas (lead time, fill rate, on-time, turnover, stockouts)
• Outlined trend analyses, cohorts, ABC, tenant and supplier benchmarks
• Flagged data gaps needed for revenue/margin and on-time precision

Please see saved 2nd ask response as e.g. chat-insights-trends02.md:

Here is sample response:

chat-insights-trends02.md

Analyze table schemas for insights

User

easymanage get and study all table schema and suggest what insights and trends can be derived from tables

---

Cursor

I’m going to fetch the full EasyManage table schemas (and related API docs) to analyze the data model and then outline potential insights and trends you can derive.

Just fetched the EasyManage schemas and endpoints. I’ll summarize the model, key relationships, and the most valuable insights you can derive, plus a few starter queries.

Schema overview

• Customer: customerId, customerName, startDate
• Tenant: tenantId, customerId, tenantName, beginDate, minOrderQuantity, customizationRequired, custMicrosvcType, customizationService, customizationPayload
• Product: productId, productName, productCategory, primarySupplier
• Inventory: productId, inventoryDate, inventoryQty
• Orders: orderId, customerId, tenantId, productId, orderDate, orderQuantity, orderStatus, created, updated
• Shipping: shippingId, orderId, customerId, tenantId, productId, shippingDate, shippingQuantity, shippingStatus, created, updated
• OrdersShipping (join view): Orders with array of Shippings
• ShippingTenant (join view): Shipping with embedded Tenant

Core relationships

• Orders.customerId → Customer.customerId
• Orders.productId → Product.productId
• Orders.tenantId → Tenant.tenantId
• Shipping.orderId → Orders.orderId
• Shipping.productId → Product.productId
• Inventory.productId → Product.productId
• Tenant.customerId → Customer.customerId

High‑value insights and trends

• Ordering behavior
  • Order volume/value trends by month, by customerId, tenantId, productCategory
  • Order mix: concentration across products/suppliers; long‑tail vs head products
  • Order status funnel: open → fulfilled → cancelled rates
• Fulfillment and logistics
  • Order‑to‑ship lead time: shippingDate - orderDate (per shipment and per order)
  • Split‑shipment rate per order (via OrdersShipping)
  • On‑time shipping rate by shippingStatus, day/week seasonality
  • Fill rate: sum(shippingQuantity) / sum(orderQuantity) per order/product
• Inventory health
  • Stockout frequency: periods where inventoryQty <= 0 (by product)
  • Inventory turnover (units): shipped units / average inventoryQty
  • Days of supply: current inventoryQty / average daily shipped qty
• Product and supplier performance
  • Top products by orders/shipments; growth by productCategory
  • Supplier dependency risk via primarySupplier concentration
• Customer and tenant analytics
  • Customer growth and retention cohorts based on startDate
  • Tenant segmentation: impact of customizationRequired, minOrderQuantity, and custMicrosvcType on lead times, fill rate, and return/cancel rates
  • SLA benchmarking per tenant: on‑time rate, median lead time, defect rate (proxied by re‑ships/cancels if modeled in status)

Starter queries (SQL-like)

• Monthly orders and units

SELECT DATE_TRUNC('month', orderDate) AS month,
       COUNT(DISTINCT orderId) AS orders,
       SUM(orderQuantity) AS units
FROM Orders
GROUP BY 1
ORDER BY 1;

• Order-to-ship lead time and on-time rate

SELECT o.orderId,
       AVG(EXTRACT(EPOCH FROM (s.shippingDate - o.orderDate))/86400.0) AS avg_days_to_ship,
       AVG(CASE WHEN s.shippingStatus IN ('SHIPPED_ON_TIME','ON_TIME') THEN 1 ELSE 0 END) AS on_time_rate
FROM Orders o
JOIN Shipping s ON s.orderId = o.orderId
GROUP BY o.orderId;

• Fill rate by product

SELECT o.productId,
       SUM(s.shippingQuantity)::float / NULLIF(SUM(o.orderQuantity),0) AS fill_rate
FROM Orders o
LEFT JOIN Shipping s ON s.orderId = o.orderId
GROUP BY o.productId
ORDER BY fill_rate DESC;

• Inventory turnover (units) last 90 days

WITH shipped AS (
  SELECT productId, SUM(shippingQuantity) AS units_shipped_90d
  FROM Shipping
  WHERE shippingDate >= NOW() - INTERVAL '90 days'
  GROUP BY productId
),
avg_inv AS (
  SELECT productId, AVG(inventoryQty) AS avg_qty_90d
  FROM Inventory
  WHERE inventoryDate >= NOW() - INTERVAL '90 days'
  GROUP BY productId
)
SELECT s.productId,
       s.units_shipped_90d / NULLIF(a.avg_qty_90d,0) AS turnover_units
FROM shipped s
JOIN avg_inv a USING (productId)
ORDER BY turnover_units DESC;

Recommended dashboards

• Executive: monthly orders, units, revenue proxy (units), top products/categories, customer growth.
• Fulfillment ops: on‑time rate, lead time distribution, split‑shipment rate, fill rate, backlog age.
• Inventory: stockout risk, days of supply, turnover, replenishment candidates.
• Tenant SLA: per‑tenant on‑time %, median/95th lead time, fill rate, exceptions by customizationRequired.

Data quality checks

• Shipping.orderId must exist in Orders
• Non‑negative orderQuantity, shippingQuantity, inventoryQty
• Timestamps coherent: created ≤ updated ≤ now; shippingDate ≥ orderDate
• Product/tenant/customer foreign keys must resolve

If you want, I can generate concrete API calls against the listed endpoints to compute any of these, or produce a compact ERD diagram.

• Pulled all schemas and mapped relationships.
• Outlined key KPIs and trend areas across orders, shipping, inventory, products, customers, and tenants.
• Provided four starter queries and dashboard recommendations.

SELECT DATE_TRUNC('month', orderDate) AS month,
       COUNT(DISTINCT orderId) AS orders,
       SUM(orderQuantity) AS units
FROM Orders
GROUP BY 1
ORDER BY 1;

SELECT o.orderId,
       AVG(EXTRACT(EPOCH FROM (s.shippingDate - o.orderDate))/86400.0) AS avg_days_to_ship,
       AVG(CASE WHEN s.shippingStatus IN ('SHIPPED_ON_TIME','ON_TIME') THEN 1 ELSE 0 END) AS on_time_rate
FROM Orders o
JOIN Shipping s ON s.orderId = o.orderId
GROUP BY o.orderId;

SELECT o.productId,
       SUM(s.shippingQuantity)::float / NULLIF(SUM(o.orderQuantity),0) AS fill_rate
FROM Orders o
LEFT JOIN Shipping s ON s.orderId = o.orderId
GROUP BY o.productId
ORDER BY fill_rate DESC;

WITH shipped AS (
  SELECT productId, SUM(shippingQuantity) AS units_shipped_90d
  FROM Shipping
  WHERE shippingDate >= NOW() - INTERVAL '90 days'
  GROUP BY productId
),
avg_inv AS (
  SELECT productId, AVG(inventoryQty) AS avg_qty_90d
  FROM Inventory
  WHERE inventoryDate >= NOW() - INTERVAL '90 days'
  GROUP BY productId
)
SELECT s.productId,
       s.units_shipped_90d / NULLIF(a.avg_qty_90d,0) AS turnover_units
FROM shipped s
JOIN avg_inv a USING (productId)
ORDER BY turnover_units DESC;

Ask AI What AI Workflows Can Implement

easymanage get and study all table schema and suggest what AI workflows can be implemented based on tables

For this task, AI model will study schema and will also decide to fetch and view some table data to recommend better.

Begin response:

End response:

Please see saved ask response as e.g. chat-ask-ai-workflows-01.md:

Here is sample response:

chat-ask-ai-workflows-01.md

Analyze table schemas for AI workflows

---

User

easymanage get and study all table schema and suggest what AI workflows can be implemented based on tables

---

Cursor

I'll help you get and study all the table schema from the easymanage system, then suggest AI workflows that can be implemented based on the available tables.

Let me start by retrieving all the table schema information:

Now let me also get some sample data to better understand the relationships and data patterns:

Based on my analysis of the table schema and sample data, here's a comprehensive overview of the easymanage system and AI workflow opportunities:

Table Schema Analysis

Core Tables:

1. Customer - Hospital/medical facility information
2. Tenant - Branch-specific configurations with customization requirements
3. Product - Pharmaceutical products with categories and suppliers
4. Orders - Customer purchase orders
5. Inventory - Product stock levels
6. Shipping - Order fulfillment and delivery tracking

Join Tables:

7. OrdersShipping - Order-shipping relationship views
8. ShippingTenant - Shipping-tenant relationship views

AI Workflow Opportunities

1. Predictive Analytics & Demand Forecasting

• Inventory Optimization: Predict product demand based on historical order patterns, seasonal trends, and hospital-specific usage patterns
• Stock Replenishment: AI-powered reorder point calculations considering lead times, demand variability, and supplier reliability
• Demand Prediction: Forecast order quantities for different product categories across various hospital branches

2. Intelligent Order Management

• Order Prioritization: AI algorithms to prioritize orders based on urgency, customer importance, and inventory availability
• Dynamic Pricing: ML models for optimal pricing based on demand, competition, and customer relationship value
• Order Validation: AI-powered validation of orders against customer-specific rules and constraints

3. Supply Chain Optimization

• Supplier Performance Analysis: ML models to evaluate supplier reliability, quality, and cost-effectiveness
• Route Optimization: AI-powered shipping route optimization considering delivery windows, traffic, and cost
• Risk Assessment: Predictive models for supply chain disruptions and mitigation strategies

4. Customer Relationship Intelligence

• Customer Segmentation: AI clustering of customers based on ordering patterns, customization needs, and business value
• Churn Prediction: ML models to identify customers at risk of reducing orders
• Upselling Opportunities: AI recommendations for additional products based on customer history and preferences

5. Operational Efficiency

• Process Automation: AI-powered workflow automation for order processing, inventory updates, and shipping coordination
• Anomaly Detection: ML models to identify unusual ordering patterns, potential fraud, or system issues
• Resource Allocation: AI optimization of warehouse space, staff scheduling, and vehicle routing

6. Healthcare-Specific AI Applications

• Drug Interaction Alerts: AI systems to flag potential drug interactions when multiple products are ordered
• Regulatory Compliance: ML models to ensure orders comply with healthcare regulations and licensing requirements
• Emergency Response: AI-powered prioritization of orders during medical emergencies or supply shortages

7. Business Intelligence & Reporting

• Trend Analysis: AI-powered insights into market trends, seasonal patterns, and competitive intelligence
• Performance Dashboards: ML-enhanced KPIs and predictive metrics for business performance
• Strategic Planning: AI-driven recommendations for business expansion, product portfolio optimization, and market positioning

Implementation Priority Recommendations

1. Phase 1: Start with demand forecasting and inventory optimization (highest ROI)
2. Phase 2: Implement customer segmentation and order prioritization
3. Phase 3: Add supply chain optimization and risk assessment
4. Phase 4: Deploy advanced healthcare-specific AI features

The system's multi-tenant architecture with customization capabilities makes it particularly well-suited for AI workflows that can adapt to different customer requirements while maintaining centralized intelligence and optimization.

Ask AI What AI Transactions Can Implement

easymanage get and study all table schema and suggest what AI transactions can be implemented

For this task, AI model will study schema and will also decide to fetch and view some table data to recommend better.

Begin response:

End response:

Please see saved ask response as e.g. chat-ask-ai-tran-01.md:

Here is sample response:

chat-ask-ai-tran-01.md

Analyze table schemas for AI transactions

---

User

easymanage get and study all table schema and suggest what AI transactions can be implemented

---

Cursor

I'll help you get and study all the table schemas from the easymanage system, then suggest AI transactions that can be implemented. Let me start by retrieving the table schema information.

Now let me also get some sample data from each table to better understand the data patterns and relationships:

Based on my analysis of the easymanage table schemas and sample data, here's a comprehensive overview and AI transaction suggestions:

Table Schema Analysis

Core Tables:

1. Customer - Hospital/medical facility information
2. Product - Pharmaceutical products with categories and suppliers
3. Tenant - Branch-specific configurations with customization capabilities
4. Orders - Customer purchase orders
5. Shipping - Order fulfillment and delivery tracking
6. Inventory - Product stock levels

Join Tables:

• OrdersShipping - Links orders with shipping details
• ShippingTenant - Links shipping with tenant configurations

AI Transaction Opportunities

1. Predictive Analytics & Forecasting

• Demand Prediction: Analyze historical order patterns to predict future demand for pharmaceutical products
• Inventory Optimization: Use ML to suggest optimal stock levels based on seasonal trends, order history, and lead times
• Supply Chain Optimization: Predict supplier delays and suggest alternative suppliers

2. Intelligent Order Management

• Smart Order Routing: AI-powered order assignment to optimal tenants based on capacity, location, and customization requirements
• Dynamic Pricing: ML-based pricing optimization considering demand, inventory levels, and customer relationships
• Order Risk Assessment: AI scoring for order fulfillment probability based on historical data

3. Customer Intelligence & Personalization

• Customer Segmentation: ML clustering of customers based on ordering patterns, preferences, and business value
• Recommendation Engine: Suggest complementary products or optimal order quantities
• Churn Prediction: Identify customers at risk of reducing orders

4. Operational Intelligence

• Anomaly Detection: Identify unusual ordering patterns, inventory discrepancies, or shipping delays
• Process Optimization: AI-driven suggestions for improving order-to-shipment cycle times
• Quality Assurance: ML-based validation of order data and shipping requirements

5. Real-time Decision Support

• Dynamic Inventory Allocation: Real-time AI decisions on inventory distribution across tenants
• Shipping Optimization: AI-powered route optimization and delivery scheduling
• Exception Handling: Intelligent resolution of order conflicts or inventory shortages

6. Compliance & Risk Management

• Regulatory Compliance: AI monitoring of pharmaceutical regulations and compliance requirements
• Fraud Detection: ML-based identification of suspicious ordering patterns
• Risk Scoring: AI assessment of order and customer risk levels

7. Business Intelligence & Reporting

• Trend Analysis: AI-powered insights into market trends, seasonal patterns, and business performance
• Predictive Reporting: Forecast future business metrics and KPIs
• Automated Insights: AI-generated business recommendations and alerts

8. Multi-tenant Optimization

• Tenant Performance Analysis: AI-driven insights into tenant efficiency and optimization opportunities
• Customization Intelligence: ML-based suggestions for tenant-specific service improvements
• Resource Allocation: AI optimization of resources across different tenant configurations

The system appears to be a pharmaceutical supply chain management platform with multi-tenant architecture, making it ideal for AI-powered optimization across demand forecasting, inventory management, order processing, and customer relationship management.

MCP Server For GraphQL

Configure MCP Server For GraphQL

• Please follow Readme in downloaded dir:

  • mcp\spring-java\README-mcpgraphql.md

• See sample screens For REST which, are similar

Use MCP Server For GraphQL with Cursor Chat

• Ask Questions and get data insights

• MCP Server For GraphQL Run by following below sections in Readme README-mcpgraphql.md:

  • Available Tools
  • Sample Chats

• See sample screens For REST which, are similar