Advanced Features

Sophisticated capabilities for complex workflows and understanding results.

Workflow Execution System

Automated execution of multi-step workflows with parameter substitution and result tracking.

What Are Workflows?

Pre-built sequences of commands that work together to accomplish a goal.

Example Workflow - Data Quality Check:

1. Profile data (dataProfile)
   ↓
2. Find duplicates (duplicateDetection)
   ↓
3. Validate data (dataValidator)
   ↓
4. Get interpretation (interpret_result)
   ↓
5. Generate report

Executing a Workflow

MCP Tool: hana_execute_workflow

{
  "workflowId": "data-quality-check",
  "parameters": {
    "schema": "SALES",
    "table": "CUSTOMERS",
    "stopOnError": false
  }
}

Output includes:

• Results from each step
• Execution time per step
• Any errors encountered
• Summary and recommendations

Previewing Before Execution

MCP Tool: hana_preview_workflow

{
  "workflowId": "data-quality-check",
  "parameters": {
    "schema": "SALES",
    "table": "CUSTOMERS"
  }
}

Shows:

• All steps in order
• Parameters for each step
• Expected execution
• No actual commands run

Built-In Workflows

The MCP Server includes 20+ professional workflows:

1. Data Validation Workflows

data-quality-check (5 steps)

• Profile → Duplicates → Validation → Interpretation → Report

data-integrity-audit (6 steps)

• Inspect → Compare → Validate → Referential check → Analysis → Report

data-cleansing (7 steps)

• Profile → Identify issues → Mask sensitive → Clean → Transform → Validate → Report

2. Schema Management Workflows

schema-comparison (4 steps)

• Inspect source → Inspect target → Compare → Generate DDL

schema-migration (6 steps)

• Validate source → Compare → Generate DDL → Test → Migrate → Verify

schema-clone (5 steps)

• Inspect source → Clone → Verify structures → Build indexes → Validate

3. Performance Analysis Workflows

performance-baseline (5 steps)

• Health check → Memory analysis → Expensive statements → Index review → Report

performance-optimization (7 steps)

• Baseline → Hotspot analysis → Index test → Recommendations → Implement → Test → Verify

resource-optimization (4 steps)

• Memory analysis → Identify large tables → Reclaim space → Verify

4. Data Operations Workflows

safe-import (5 steps)

• Dry run → Review errors → Validate → Import → Verify

safe-export (4 steps)

• Verify source → Configure format → Export → Validate

data-migration (6 steps)

• Export → Prepare target → Import → Validate → Compare → Report

5. Troubleshooting Workflows

connection-diagnosis (4 steps)

• Test connection → Check permissions → Verify network → Get info

performance-diagnosis (5 steps)

• Health check → Memory → Expensive queries → Hotspots → Recommendations

Parameter Substitution

Workflows support parameter templates using <parameter-name>:

{
  "workflow": {
    "steps": [
      {
        "command": "hana_inspectTable",
        "parameters": {
          "table": "<table>",
          "schema": "<schema>"
        }
      },
      {
        "command": "hana_dataProfile",
        "parameters": {
          "table": "<table>",
          "schema": "<schema>"
        }
      }
    ]
  }
}

When executing, provide actual values:

{
  "workflowId": "my-workflow",
  "parameters": {
    "table": "CUSTOMERS",
    "schema": "SALES"
  }
}

Parameters are automatically substituted in each step.

Error Handling in Workflows

Control how workflows handle errors:

Continue on errors:

{
  "workflowId": "data-quality-check",
  "parameters": { ... },
  "stopOnError": false
}

Stop on first error:

{
  "workflowId": "data-quality-check", 
  "parameters": { ... },
  "stopOnError": true
}

Output shows:

{
  "steps": [
    {
      "step": 1,
      "command": "dataProfile",
      "status": "success",
      "duration": 2.5
    },
    {
      "step": 2,
      "command": "duplicateDetection",
      "status": "success",
      "duration": 1.8
    }
  ],
  "totalDuration": 4.3,
  "errors": []
}

Result Interpretation (hana_interpret_result)

Transform raw command results into AI-friendly insights and recommendations.

What It Does

Analyzes command output and provides:

1. Summary - High-level interpretation
2. Insights - Key findings and patterns
3. Recommendations - Actionable suggestions
4. Concerns - Issues requiring attention
5. Key Metrics - Important numbers

How to Use

Input:

{
  "command": "dataProfile",
  "result": "command output text or object"
}

Output:

{
  "command": "dataProfile",
  "summary": "4,250 rows analyzed; moderate data quality issues",
  "insights": [
    "15% NULL values in CUSTOMER_NAME",
    "127 duplicate EMAIL entries (2.9%)",
    "43 invalid DATE_OF_BIRTH values (1.0%)"
  ],
  "recommendations": [
    {
      "priority": "high",
      "action": "Clean NULL values in CUSTOMER_NAME",
      "impact": "Improves data quality by 15%",
      "nextCommand": "hana_dataValidator"
    }
  ],
  "concerns": [
    {
      "level": "critical",
      "issue": "High duplicate rate in EMAIL column",
      "action": "Run duplicateDetection to clean"
    }
  ],
  "metrics": {
    "totalRows": 4250,
    "nullPercentage": 1.5,
    "duplicateCount": 127,
    "validationErrors": 43
  }
}

Command-Specific Interpretation

Data Profile Results

• Data quality issues detected
• NULL value percentages
• Duplicate identification
• Data type mismatches
• Range and distribution analysis

Provides:

• Data quality score
• Issues ranked by severity
• Cleaning recommendations
• Next validation steps

Memory Analysis Results

• Memory usage concentration
• Largest tables and indexes
• Memory growth trends
• Fragmentation issues

Provides:

• Resource optimization suggestions
• Partitioning recommendations
• Compression opportunities
• Reclaim recommendations

Health Check Results

• System status and alerts
• Critical warnings
• Performance issues
• Resource constraints

Provides:

• Problem diagnosis
• Immediate actions
• Investigations needed
• Prevention tips

Expensive Statements Results

• Long-running queries
• Resource-intensive operations
• Query patterns
• Performance bottlenecks

Provides:

• Optimization suggestions
• Index recommendations
• Query rewrite options
• Monitoring next steps

Interpretation Examples

Example 1: Data Profile

Raw Output:
" Rows: 10000
  Columns: 8
  NULL values: 1500 (15%)
  Duplicates: 250
  Errors: 45"

Interpreted:
Summary: "Data quality is moderate with significant issues"
Insights:
- "High NULL value percentage (15%) in some columns"
- "Duplicate records found (2.5% of dataset)"
- "45 validation errors detected"
Recommendations:
- Priority: high → "Clean NULL values first"
- Priority: high → "Remove duplicate records"
- Priority: medium → "Fix validation errors"

Example 2: Memory Analysis

Raw Output:
"TOP MEMORY ALLOCATIONS
1. TABLE CUSTOMERS - 800 MB (45%)
2. INDEX ON_CUSTOMERS_ID - 300 MB (17%)
3. TABLE ORDERS - 600 MB (34%)"

Interpreted:
Summary: "Memory concentrated in two large tables"
Insights:
- "CUSTOMERS table using 45% of total memory"
- "Combined table memory: 79% of total"
Recommendations:
- Priority: high → "Consider partitioning CUSTOMERS table"
- Priority: medium → "Review index on CUSTOMERS"
- Priority: medium → "Analyze ORDERS table growth"
Concerns:
- Memory concentration risk if tables grow
- Limited headroom for other operations

Using Interpreted Results

1. Get recommendations - Understand what to do next
2. Prioritize actions - High priority first
3. Chain commands - Use suggested next commands
4. Track metrics - Monitor key numbers
5. Plan tuning - Use insights for optimization

Documentation Search Integration

Access all 279 project documentation pages directly from MCP.

Searching Documentation

MCP Tool: hana_search_docs

{
  "query": "import CSV data",
  "category": "commands",
  "docType": "command",
  "limit": 5
}

Returns:

{
  "results": [
    {
      "title": "Import Command Guide",
      "path": "02-commands/data-tools/import.md",
      "category": "commands",
      "docType": "command",
      "relevance": 99,
      "excerpt": "Import data from CSV, Excel, or TSV files...",
      "url": "https://sap-samples.github.io/hana-developer-cli-tool-example/..."
    }
  ]
}

Getting Full Documentation

MCP Tool: hana_get_doc

{
  "path": "02-commands/data-tools/import.md"
}

Returns:

• Complete markdown content
• Document metadata
• Table of contents (headings)
• Related links
• Full website URL

Finding Documentation by Category

MCP Tool: hana_list_doc_categories

Returns:

• All 9+ categories
• Number of documents per category
• Sample documents from each

Categories:

• Getting Started (5 docs)
• Commands (80+ docs)
• Features (15 docs)
• API Reference (10 docs)
• Development (20 docs)
• Troubleshooting (8 docs)
• Examples (50+ docs)

Documentation Search Workflow

1. User: "How do I import a CSV file?"
   ↓
2. System: Calls hana_search_docs
   ↓
3. Returns: Top 5 import-related docs
   ↓
4. User: Selects most relevant result
   ↓
5. System: Calls hana_get_doc with path
   ↓
6. Returns: Full import command documentation
   ↓
7. Shows: Examples, parameters, troubleshooting

Advanced Scenarios

Scenario 1: Complete Data Migration

1. Preview: hana_preview_workflow("schema-migration")
   ↓
2. Execute: hana_execute_workflow("schema-migration")
   - Validate source
   - Generate DDL
   - Test migration
   - Compare schemas
   ↓
3. Interpret: hana_interpret_result("dataValidator", results)
   ↓
4. Report: Generate migration report with metrics

Scenario 2: Performance Optimization

1. Get baseline: hana_execute_workflow("performance-baseline")
   ↓
2. Analyze: hana_interpret_result("memoryAnalysis", baseline)
   ↓
3. Find issues: hana_execute_workflow("performance-diagnosis")
   ↓
4. Get recommendations: from diagnosed issues
   ↓
5. Implement optimizations
   ↓
6. Re-run baseline: hana_execute_workflow("performance-baseline")
   ↓
7. Compare: baseline before vs. after

Scenario 3: Data Quality Assurance

1. Profile data: hana_dataProfile(table)
   ↓
2. Interpret: hana_interpret_result("dataProfile", results)
   ↓
3. Find issues: recommendations and concerns
   ↓
4. Execute: hana_execute_workflow("data-cleansing")
   - Identify issues
   - Clean data
   - Validate
   ↓
5. Verify: hana_dataValidator(table)
   ↓
6. Report: Quality metrics and changes

Best Practices

1. Always Preview Before Executing

// Step 1: Preview
hana_preview_workflow("data-migration", parameters)

// Step 2: Review and confirm
// Step 3: Execute
hana_execute_workflow("data-migration", parameters)

2. Use Error Handling Wisely

{
  "stopOnError": true  // For critical workflows
}

{
  "stopOnError": false  // For bulk operations with retry
}

3. Interpret All Results

Every command result should be interpreted:

hana_interpret_result(command, result)

Gets insights, recommendations, and next steps.

4. Chain Workflows Logically

Diagnosis → Analysis → Action → Verification → Report

Each step builds on previous results.

5. Track Metrics Over Time

Use key metrics to measure success:

• Before/after comparisons
• Performance improvements
• Data quality scores
• Resource utilization

Next Steps

• Discovery Tools - Finding the right commands
• Prompts and Resources - MCP resources and guides
• Implementation Phases - Technical details