▶ COMPLETE AI WORKFLOW PATTERNS
Claude: resource → prompt → sampling → action - combining all four MCP primitives
┌──────────────────────────────────────────────────────────────────┐
│ COMPLETE WORKFLOW CHAIN │
├──────────────────────────────────────────────────────────────────┤
│ │
│ 1. RESOURCE 2. PROMPT 3. SAMPLING 4. ACTION │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌───────────┐ │
│ │ Load data │─►│ Template │─►│ AI analysis │►│ Execute │ │
│ │ from URI │ │ with args │ │ & decision │ │ tool call │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ └───────────┘ │
│ │
│ Claude orchestrates the entire chain intelligently │
└──────────────────────────────────────────────────────────────────┘
🎮 LIVE DEMO WORKFLOWS
📊 WORKFLOW 1: AI DATA ANALYSIS
DEMONSTRATION: Use
sampleLLM to write markdown summary from resource
$ mcp-inspector
> Multi-step workflow demonstration
STEP 1: Load Resource
> Browse to test://static/resource/42
> Data: {"metrics": [...], "timestamp": "2025-11-19T10:45:00Z"}
STEP 2: Apply Prompt Template
> Select resource_prompt template
> Set resource_uri: "test://static/resource/42"
> Generate analysis prompt
STEP 3: AI Sampling
> Call sampleLLM with generated prompt
> AI analyzes resource data
> Produces structured markdown summary
STEP 4: Action (Optional)
> Save summary as new resource
> Trigger notification workflow
> Update dashboard display
[RESULT] Complete AI-driven data analysis pipeline
🎨 WORKFLOW 2: MULTI-MODAL CREATION
DEMONSTRATION: Combine
complex_prompt + getTinyImage to mix modalities
STEP 1: Complex Prompt Generation
> Execute complex_prompt with creative parameters
> Generate detailed image description
STEP 2: Image Generation
> Call getTinyImage with AI-generated description
> Create visual content based on prompt
STEP 3: Multi-Modal Output
> Combine text analysis + generated image
> Create rich content presentation
[RESULT] AI-generated content with both text and visuals
▶ PRODUCTION WORKFLOW PATTERNS
🔄 AUTOMATED CODE REVIEW
```python
# Multi-server workflow example
async def automated_code_review_workflow():
# 1. Resource: Get changed files from Git
changed_files = await git_server.call_tool("git_diff", {"format": "unified"})
# 2. Prompt: Apply code review template
review_prompt = await prompt_server.get_prompt(
"code_review_template",
{"diff_content": changed_files, "language": "python"}
)
# 3. Sampling: AI analyzes code quality
ai_review = await sampling_server.call_tool(
"sampleLLM",
{"prompt": review_prompt}
)
# 4. Action: Post review comments
await git_server.call_tool(
"post_review_comment",
{"content": ai_review, "pr_id": pr_id}
)
```
📈 REAL-TIME MONITORING PIPELINE
```yaml
workflow_name: "performance_monitoring"
triggers:
- schedule: "*/5 minutes"
- webhook: "/alerts/performance"
steps:
1. resource_collection:
- server: monitoring_server
- tool: collect_metrics
- output: metrics_resource_uri
2. analysis_prompt:
- server: prompt_server
- template: performance_analysis
- args: {resource_uri: "${step1.metrics_resource_uri}"}
3. ai_assessment:
- server: ai_server
- tool: sampleLLM
- input: "${step2.rendered_prompt}"
4. alert_action:
- server: notification_server
- tool: send_alert
- condition: "${step3.severity} > 'warning'"
```
🎮 LIVE DEMO: SUBSCRIPTION WORKFLOWS
DEMONSTRATION: Subscribed resource auto-refresh visible in Inspector
$ mcp-inspector
> Navigate to Resources tab
> Subscribe to test://static/resource/42
> Watch real-time updates trigger workflow chains
[T+00s] Resource updated → Trigger analysis workflow
[T+00s] AI processes new data → Generate insights
[T+01s] Workflow completes → Update dashboard
[T+05s] Resource updated → Repeat cycle
[AUTOMATION] Fully automated reactive workflows
[EFFICIENCY] Only processes when data changes
[SCALABILITY] Multiple subscribers, single source
Subscription Benefits:
- Event-Driven: Only executes when data changes
- Resource Efficient: No polling overhead
- Real-Time Response: Immediate workflow triggers
- Parallel Processing: Multiple workflows can subscribe
▶ ENTERPRISE INTEGRATION PATTERNS
🔗 API GATEWAY INTEGRATION
MCP servers behind enterprise API management
📊 WORKFLOW ORCHESTRATION
Integration with tools like Airflow, Temporal
🔐 IDENTITY FEDERATION
SSO integration with corporate identity providers
📈 BUSINESS INTELLIGENCE
AI workflows feeding into BI dashboards
▶ OBSERVABILITY IN WORKFLOWS
├── Distributed Tracing │ Follow requests across multiple servers
├── Workflow Metrics │ Success rates, execution times, bottlenecks
├── Business Metrics │ ROI, productivity gains, quality improvements
├── Error Analysis │ Failure modes, recovery patterns, prevention
└── Performance Tuning │ Optimization opportunities, resource usage