Advanced Features

Sophisticated capabilities for complex workflows and understanding results.

Workflow Templates

Pre-built sequences of commands that work together to accomplish a goal. The AI agent orchestrates execution — workflow templates provide the sequence and recommended parameters, while the LLM handles execution and decision-making between steps.

What Are Workflows?

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

Browsing Workflows

MCP Tool: hana_workflows

Returns a list of all available workflow templates with descriptions and step counts.

MCP Tool: hana_workflow_by_id

{
  "id": "data-quality-check"
}

Returns:

• All steps in order with commands and parameters
• Parameter templates using <parameter-name> substitution
• Expected outcomes per step
• Tips and best practices

MCP Tool: hana_search_workflows

{
  "tag": "performance"
}

Search workflows by tag (e.g., data-quality, performance, security, backup, migration).

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

Workflow templates use parameter placeholders with <parameter-name> syntax. When the AI agent executes each step, it substitutes the actual values:

{
  "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"
  }
}

The AI agent substitutes actual values when calling each tool in sequence.

Error Handling in Workflows

The AI agent decides how to handle errors between steps — it can skip failed steps, retry with different parameters, or stop and report issues. Workflow templates include guidance on which steps are critical vs. optional.

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

{
  "query": "import CSV data",
  "scope": "docs",
  "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 Resource: hana://docs/categories

Documentation categories are available as an MCP resource (not a tool), keeping the tool list concise while still providing browsable metadata.

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
   ↓
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. Browse: hana_workflow_by_id("schema-migration")
   ↓
2. Execute steps guided by the workflow template:
   - Validate source schema
   - 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 workflow: hana_workflow_by_id("performance-baseline")
   ↓
2. Execute baseline steps: hana_healthCheck, hana_memoryAnalysis, etc.
   ↓
3. Analyze: hana_interpret_result("memoryAnalysis", baseline)
   ↓
4. Get diagnosis workflow: hana_workflow_by_id("performance-diagnosis")
   ↓
5. Execute diagnosis steps and get recommendations
   ↓
6. Implement optimizations
   ↓
7. Re-run baseline steps and compare 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. Get workflow: hana_workflow_by_id("data-cleansing")
   ↓
5. Execute cleansing steps:
   - Identify issues
   - Clean data
   - Validate
   ↓
6. Verify: hana_dataValidator(table)
   ↓
7. Report: Quality metrics and changes

Best Practices

1. Review Workflow Templates First

# Step 1: Browse the workflow
hana_workflow_by_id("data-migration")

# Step 2: Review the steps and parameters
# Step 3: Execute each step, adapting as needed

2. Handle Errors Between Steps

The AI agent should check results between workflow steps and decide whether to continue, retry, or stop based on the outcome.

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