# Apify Dataset Strategy for Jakarta Migration MCP Server
This document analyzes whether to use Apify datasets for storing MCP tool outputs and provides recommendations.
## Current Architecture
**Current Approach**: MCP server returns JSON responses directly via MCP protocol
- Real-time responses to AI assistants
- No persistent storage
- Results only available during active session
- No historical tracking
## Should We Use Apify Datasets?
### ✅ **YES - Recommended for Production**
Using Apify datasets provides significant value, especially for enterprise users and audit/compliance requirements.
## Advantages of Using Apify Datasets
### 1. **Persistent Storage & Historical Tracking** ⭐⭐⭐⭐⭐
**Benefit**: Results are stored and accessible after the MCP session ends
**Use Cases**:
- Audit trails for compliance
- Track migration progress over time
- Compare results across multiple runs
- Historical analysis of project readiness
**Example**:
```json
// Stored in dataset
{
"runId": "abc123",
"timestamp": "2026-01-06T10:00:00Z",
"projectPath": "/path/to/project",
"tool": "analyzeJakartaReadiness",
"readinessScore": 0.75,
"blockers": 2,
"recommendations": 8
}
```
### 2. **Better Apify Console UI** ⭐⭐⭐⭐
**Benefit**: Structured data display in Apify Console
**Features**:
- Tabular view of all analysis results
- Filterable and sortable results
- Export to CSV, JSON, Excel
- Visual charts and graphs (if configured)
**User Experience**:
- Users can browse all previous analyses
- Compare results side-by-side
- Export reports for stakeholders
### 3. **API Access & Integration** ⭐⭐⭐⭐⭐
**Benefit**: Results accessible via Apify API
**Use Cases**:
- CI/CD integration (check migration readiness in pipeline)
- Dashboard integration (monitor migration progress)
- Webhook triggers (notify on blockers found)
- Integration with other tools/workflows
**Example API Call**:
```bash
GET https://api.apify.com/v2/datasets/{datasetId}/items
# Returns all stored analysis results
```
### 4. **Sharing & Collaboration** ⭐⭐⭐⭐
**Benefit**: Results can be shared with team members
**Use Cases**:
- Share migration analysis with team
- Review results with stakeholders
- Export reports for management
- Collaborative migration planning
### 5. **Billing & Usage Tracking** ⭐⭐⭐
**Benefit**: Track usage for billing and analytics
**Use Cases**:
- Count tool executions for billing
- Analyze usage patterns
- Identify popular features
- Optimize pricing based on usage
### 6. **Error Recovery & Debugging** ⭐⭐⭐
**Benefit**: Stored results help with debugging
**Use Cases**:
- Debug failed migrations
- Compare successful vs failed runs
- Identify patterns in errors
- Support troubleshooting
## Disadvantages of Using Apify Datasets
### 1. **Additional Complexity** ⭐⭐
**Cost**: Need to integrate Apify SDK and push data
**Impact**:
- Additional dependency (Apify Java SDK)
- Code changes to push data after tool execution
- Error handling for dataset operations
- Slight performance overhead
### 2. **Storage Costs** ⭐
**Cost**: Apify charges for dataset storage
**Impact**:
- Minimal cost for small datasets
- Can add up for high-volume usage
- Need to manage dataset retention
### 3. **Not Required for MCP Protocol** ⭐
**Note**: MCP protocol already handles responses
**Impact**:
- Datasets are optional enhancement
- MCP clients get responses directly
- Datasets are for persistence, not real-time communication
## Recommended Hybrid Approach
### Strategy: **Store Results + Return via MCP**
Store results in Apify datasets **in addition to** returning via MCP protocol:
```java
@Tool(name = "analyzeJakartaReadiness")
public String analyzeJakartaReadiness(String projectPath) {
// ... perform analysis ...
// 1. Build response (for MCP client)
String response = buildReadinessResponse(report);
// 2. Store in Apify dataset (for persistence)
if (isApifyEnvironment()) {
apifyDatasetService.pushResult("readiness-analysis", {
"timestamp": Instant.now(),
"projectPath": projectPath,
"readinessScore": report.readinessScore().score(),
"blockers": report.blockers().size(),
"recommendations": report.recommendations().size(),
"fullReport": report // Store complete report
});
}
// 3. Return to MCP client (real-time)
return response;
}
```
### Benefits of Hybrid Approach:
1. ✅ **Real-time responses** - MCP clients get immediate results
2. ✅ **Persistent storage** - Results stored for later access
3. ✅ **Best of both worlds** - No compromise on either front
4. ✅ **Optional storage** - Only store when in Apify environment
## Implementation Plan
### Phase 1: Basic Dataset Storage (Recommended)
**Store essential results**:
- Tool name and timestamp
- Project path
- Key metrics (readiness score, blocker count, etc.)
- Status (success/error)
**Dataset Schema**:
```json
{
"actorSpecification": 1,
"fields": {
"type": "object",
"properties": {
"timestamp": { "type": "string", "format": "date-time" },
"tool": { "type": "string" },
"projectPath": { "type": "string" },
"status": { "type": "string", "enum": ["success", "error"] },
"readinessScore": { "type": "number" },
"blockerCount": { "type": "integer" },
"recommendationCount": { "type": "integer" }
}
},
"views": {
"overview": {
"title": "Migration Analysis Results",
"transformation": {
"fields": ["timestamp", "tool", "projectPath", "readinessScore", "blockerCount", "status"]
},
"display": {
"component": "table",
"properties": {
"timestamp": { "label": "Date", "format": "date" },
"tool": { "label": "Tool", "format": "text" },
"readinessScore": { "label": "Readiness Score", "format": "number" },
"blockerCount": { "label": "Blockers", "format": "number" }
}
}
}
}
}
```
### Phase 2: Enhanced Storage (Future)
**Store complete results**:
- Full analysis reports
- Detailed blocker information
- Migration plans
- Runtime verification results
## Cost Analysis
### Storage Costs
**Apify Dataset Storage**:
- First 1GB: Free
- Additional: ~$0.10/GB/month
- Typical analysis result: ~1-5 KB
- 1000 analyses = ~5 MB (negligible cost)
**Verdict**: Storage costs are minimal for typical usage.
### Development Costs
**Implementation Effort**:
- Add Apify Java SDK: 1-2 hours
- Integrate dataset pushing: 2-4 hours
- Update dataset schema: 1 hour
- Testing: 1-2 hours
**Total**: ~5-9 hours of development
## Recommendation
### ✅ **YES - Implement Dataset Storage**
**Priority**: Medium (can be added after initial release)
**Rationale**:
1. **Enterprise Value**: Audit trails and historical tracking are valuable for enterprise customers
2. **Low Cost**: Storage costs are minimal, development effort is reasonable
3. **Competitive Advantage**: Better UX in Apify Console differentiates from competitors
4. **Future-Proof**: Enables future features (analytics, reporting, integrations)
5. **Hybrid Approach**: Doesn't compromise real-time MCP responses
### Implementation Priority
1. **Phase 1** (MVP): Store basic results (tool, timestamp, key metrics)
2. **Phase 2** (Enhanced): Store complete reports with full details
3. **Phase 3** (Advanced): Add views for different analysis types, export features
## Alternative: Make It Optional
Allow users to enable/disable dataset storage:
```yaml
jakarta:
migration:
apify:
store-results: ${APIFY_STORE_RESULTS:true} # Enable/disable dataset storage
```
This gives users control over storage costs and privacy.
## Conclusion
**Recommendation**: Implement dataset storage using the hybrid approach:
- ✅ Store results in Apify datasets for persistence
- ✅ Continue returning results via MCP protocol for real-time access
- ✅ Make storage optional via configuration
- ✅ Start with basic storage, enhance later
This provides the best user experience while maintaining the real-time MCP functionality.
