The Dcycle MCP Server enables AI assistants like Claude to query your sustainability data using natural language. This transforms how teams interact with emissions data—from navigating dashboards to simply asking questions.
Coming Soon - The Dcycle MCP Server is in private beta.
Contact us for early access.
User: "How are we tracking against our 2030 reduction targets?"Claude: Based on your emissions data:Current Progress (2024 vs 2020 baseline):• Total reduction: 23% (target: 50% by 2030)• Annual reduction rate: 5.75%At current pace, you'll reach:• 34.5% reduction by 2030 (missing target by 15.5%)To hit 50% by 2030, you need to increase annualreduction rate from 5.75% to 7.2%.Top opportunities identified:1. Fleet electrification: potential 120 tCO2e/year2. Renewable energy PPA: potential 85 tCO2e/year3. Supplier engagement: potential 200 tCO2e/year
"What's our biggest source of Scope 3 emissions?""Which facilities have improved most this year?""Compare our carbon intensity to last year""What would happen to our footprint if we electrified 50% of our fleet?"
Operational Questions:
"Are there any data gaps I should address before the quarterly report?""Which suppliers should I prioritize for engagement?""Show me facilities with above-average energy intensity"
"Give me a 3-sentence summary of our sustainability performance this quarter""What are the key risks in our emissions profile?""How do we compare to industry benchmarks?"
Investment Decisions:
"What's the ROI on fleet electrification based on our current fuel costs?""Which reduction initiatives would have the biggest impact?"
"What are the total emissions for organization X in 2024?""Why did their Scope 2 emissions increase this quarter?""Are there any failed uploads I should check on?""Show me their data health status"
User: "I need to understand why our transport emissions increased 15% this quarter"Claude: Let me analyze your transport emissions for Q3 2024.Transport Emissions Analysis:• Q2 2024: 156.3 tCO2e• Q3 2024: 179.7 tCO2e• Change: +23.4 tCO2e (+15%)Contributing Factors:1. **Increased shipment volume (+8%)** - Shipments: 12,450 → 13,446 - This accounts for ~12.5 tCO2e of the increase2. **Longer average distance (+5%)** - Avg distance: 320km → 336km - Routes to new distribution center added 5.2 tCO2e3. **Vehicle mix shift** - More heavy truck usage (+12%) - Fewer van deliveries (-8%) - Net impact: +5.7 tCO2eRecommendations:1. Review new DC routes for optimization2. Consider van alternatives for shorter routes3. Evaluate rail options for long-haul segmentsWould you like me to drill into any of these areas?
Create a guide for your team on effective queries:
## Effective Questions for Dcycle AI### Be Specific❌ "Show me emissions"✅ "Show me Scope 1 emissions for 2024 by quarter"### Provide Context❌ "Why did it go up?"✅ "Why did our transport emissions increase in Q3 vs Q2?"### Ask for Comparisons✅ "Compare our 2024 emissions to 2023"✅ "How do our facilities rank by energy efficiency?"### Request Recommendations✅ "What are our biggest opportunities for reduction?"✅ "Which suppliers should we prioritize for engagement?"
┌─────────────┐ ┌─────────────┐ ┌─────────────┐│ Automated │────▶│ Dcycle │────▶│ AI ││ Upload │ │ Database │ │ Analysis │└─────────────┘ └─────────────┘ └─────────────┘ CLI/API Your data MCP + ClaudeWeekly flow:1. Monday 6 AM: Automated data upload2. Monday 9 AM: AI generates weekly summary3. Team reviews insights and takes action
Example automated insight generation:
#!/bin/bash# weekly_ai_insights.sh# Generate prompt for AI analysiscat << 'EOF' > /tmp/weekly_prompt.txtBased on the latest data upload, please provide:1. **Week-over-Week Changes** - Any significant increases or decreases - Anomalies that need attention2. **Data Quality Issues** - Missing data or gaps - Validation errors to address3. **Recommended Actions** - Top 3 items for this weekKeep the summary under 500 words.EOF# This would integrate with your AI assistant workflow# The specific implementation depends on your AI setup