# MCP Server Brainstorm: "Ask Anything About Environmental Data"
**Created:** 2026-01-22
**Status:** Brainstorming / Ideation
**Purpose:** Capture all possibilities for an AI-powered environmental data assistant via MCP
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## Vision
An AI assistant that can answer ANY question about environmental data - from simple lookups to complex multi-source analysis, with natural language understanding and contextual intelligence.
**Goal:** Install an MCP client in Cursor or Claude Code and ask any question about the data.
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## Capability Categories
### 1. Natural Language Data Querying
**The Dream:** Ask questions in plain English, get answers from the data.
| Example Question | What It Needs |
|-----------------|---------------|
| "What was PM2.5 in NYC last Tuesday?" | NL → Query translation |
| "Show me the dirtiest power plants in Texas" | Ranking + geographic filter |
| "Compare Delhi and Beijing air quality for 2024" | Cross-location time series |
| "Which countries improved air quality most since 2020?" | Trend analysis + ranking |
| "Find coal plants within 50km of any PM2.5 sensor reading above 100" | Spatial join + threshold |
**Potential Tools:**
- `ask_data_question` - Free-form natural language → structured query
- `explore_data` - Guided exploration with suggestions
- `compare` - Compare entities across dimensions
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### 2. Analytical Intelligence
**The Dream:** Not just data retrieval, but actual analysis and insight generation.
| Capability | Example |
|------------|---------|
| **Trend Detection** | "Is air quality in LA getting better or worse?" |
| **Anomaly Detection** | "Flag any unusual emission spikes this month" |
| **Forecasting** | "Predict next month's CO2 emissions for the power sector" |
| **Correlation Discovery** | "What factors correlate with high PM2.5 in industrial cities?" |
| **Attribution** | "Which facilities likely contribute to pollution at this sensor?" |
| **Benchmarking** | "How does this plant compare to others in its sector?" |
**Potential Tools:**
- `analyze_trends` - Detect patterns over time
- `find_anomalies` - Statistical outlier detection
- `explain_correlation` - Find and explain relationships
- `benchmark_entity` - Compare to peers/sector averages
- `attribute_sources` - Link effects to causes spatially/temporally
---
### 3. Geographic Intelligence
**The Dream:** Full spatial awareness and proximity-based reasoning.
**Example Queries:**
- "What emission sources are within 25km of my location?"
- "Draw an impact radius around this coal plant"
- "Find air quality sensors downwind of industrial zones"
- "Map the pollution corridor between these two cities"
- "Identify clusters of high-emission facilities"
- "Which country borders receive transboundary pollution?"
**Potential Tools:**
- `find_nearby` - Proximity queries with configurable radius
- `spatial_cluster` - Identify geographic patterns
- `impact_zone` - Calculate affected area from a source
- `map_data` - Generate GeoJSON/visualizations
- `wind_analysis` - Directional impact modeling
---
### 4. Temporal Intelligence
**The Dream:** Understanding time-based patterns and events.
| Query Type | Example |
|------------|---------|
| **Historical** | "What was China's steel sector emissions in 2015?" |
| **Comparative** | "How did COVID lockdowns affect global air quality?" |
| **Seasonal** | "What's the typical winter vs summer PM2.5 pattern in Delhi?" |
| **Event-based** | "Did air quality change after the new factory opened?" |
| **Rate of Change** | "Which facilities are reducing emissions fastest?" |
**Potential Tools:**
- `time_series_query` - Flexible temporal data retrieval
- `detect_change_points` - Find when trends shifted
- `seasonal_decompose` - Separate trend/seasonal/noise
- `event_impact` - Before/after analysis around dates
---
### 5. Cross-Source Intelligence
**The Dream:** Connect the dots between air quality, emissions, and regulatory data.
**Data Sources:**
- OpenAQ (Air Quality Measurements)
- Climate TRACE (Facility-level Emissions)
- EDGAR (National Emission Totals)
**Cross-Source Analysis:**
- Correlations between emissions and air quality
- Attribution of air quality to emission sources
- Validation/reconciliation across sources
**Key Questions This Enables:**
- "Which facilities impact this air quality sensor?"
- "Does national data match facility-level totals?"
- "How do emissions relate to measured pollution?"
**Potential Tools:**
- `correlate_sources` - Statistical relationships across sources
- `attribute_pollution` - Link emissions to air quality
- `reconcile_data` - Compare/validate across sources
- `unified_query` - Single query across all sources
---
### 6. Knowledge & Context
**The Dream:** Explain what the data means, not just what it says.
| Question | Response Type |
|----------|--------------|
| "What is PM2.5 and why does it matter?" | Educational explanation |
| "Is 35 µg/m³ PM2.5 safe?" | Health context + WHO/EPA guidelines |
| "What's the regulatory limit for CO2 emissions?" | Jurisdiction-specific regulations |
| "How does this plant's emissions compare to industry average?" | Contextual benchmarking |
| "What caused the spike in emissions in Q3?" | Investigative explanation |
**Potential Tools:**
- `explain_parameter` - What it is, health effects, thresholds
- `get_regulatory_context` - Limits and standards
- `contextualize_value` - "Is this good or bad?"
- `explain_finding` - Interpret analysis results
---
### 7. Reporting & Visualization
**The Dream:** Generate publication-ready outputs.
**Output Types:**
- **Summary Reports** - Executive summaries of any analysis
- **Time Series Charts** - Trend visualizations
- **Geographic Maps** - GeoJSON for mapping tools
- **Comparison Tables** - Side-by-side entity comparisons
- **Data Exports** - CSV, JSON, Parquet for downstream use
- **Markdown Reports** - Formatted analysis documents
**Potential Tools:**
- `generate_report` - Comprehensive analysis report
- `create_chart` - Time series, bar, scatter plots
- `export_data` - Bulk data in various formats
- `create_map` - GeoJSON with styling
---
### 8. Monitoring & Alerts
**The Dream:** Proactive monitoring, not just reactive queries.
| Capability | Example |
|------------|---------|
| **Threshold Alerts** | "Notify me when any US sensor exceeds PM2.5 of 50" |
| **Anomaly Alerts** | "Alert on any unusual emission patterns" |
| **Freshness Monitoring** | "Warn if data is more than 24 hours stale" |
| **Watchlists** | "Track these 10 facilities and summarize weekly" |
| **Compliance Tracking** | "Monitor when facilities exceed permitted levels" |
**Potential Tools:**
- `set_alert` - Create threshold/anomaly alerts
- `create_watchlist` - Track specific entities
- `check_data_freshness` - Data quality monitoring
- `get_alerts` - Retrieve triggered alerts
---
### 9. Research & Discovery
**The Dream:** Help researchers find patterns and generate hypotheses.
**Capabilities:**
- **Similarity Search** - "Find facilities similar to this one"
- **Gap Analysis** - "Where is data coverage poor?"
- **Hypothesis Generation** - "What might explain this pattern?"
- **Literature Context** - Link to relevant research
- **Data Lineage** - "Where did this measurement come from?"
**Potential Tools:**
- `find_similar` - Entity similarity search
- `identify_gaps` - Coverage analysis
- `suggest_investigation` - AI-generated research directions
- `trace_data` - Provenance and lineage
---
### 10. ESG & Compliance
**The Dream:** Support ESG reporting and compliance workflows.
| Use Case | Capability |
|----------|------------|
| **Carbon Footprint** | Calculate company/supply chain emissions |
| **ESG Scoring** | Environmental risk assessment |
| **Disclosure Support** | Data for CDP, TCFD, SASB reporting |
| **Supply Chain** | Trace emissions through supply chains |
| **Regulatory Compliance** | Check against jurisdictional requirements |
**Potential Tools:**
- `calculate_footprint` - Carbon accounting
- `assess_esg_risk` - Environmental risk scoring
- `generate_disclosure` - Reporting framework support
- `check_compliance` - Regulatory requirement checking
---
### 11. Conversational Memory & Context
**The Dream:** Build up complex analyses through conversation.
**Capabilities:**
- Remember previous queries in session
- "Now filter that by country"
- "Save this as 'my coal plant analysis'"
- "Resume where I left off yesterday"
- Build named datasets/views
**Potential Tools:**
- `save_query` - Persist named queries
- `recall_context` - Restore previous session
- `refine_query` - Iterate on previous results
---
### 12. Administrative / Pipeline
**The Dream:** Monitor and manage the data platform itself.
| Capability | Example |
|------------|---------|
| **Job Management** | "Start ingesting 2023 OpenAQ data" |
| **Pipeline Health** | "Is the ingestion pipeline healthy?" |
| **Data Quality** | "Run quality checks on Climate TRACE data" |
| **System Status** | "What's the current database load?" |
**Potential Tools:**
- `trigger_ingestion` - Start data jobs
- `check_pipeline_health` - System monitoring
- `run_quality_check` - Data validation
- `get_system_status` - Infrastructure health
---
## Wild Ideas
| Idea | Description |
|------|-------------|
| **SQL Generation** | Convert NL to SQL, show the query, let user refine |
| **Notebook Generation** | Generate Jupyter notebooks for analyses |
| **Automated Insights** | "Tell me something interesting about this data" |
| **Data Storytelling** | Generate narrative explanations of patterns |
| **Multi-modal** | Accept images (screenshots of locations) as input |
| **External Enrichment** | Pull in weather, economic, demographic data on the fly |
| **What-If Analysis** | "What if this plant reduced emissions by 20%?" |
| **Agent Mode** | Autonomously investigate until finding answers |
---
## Discussion Questions
1. **User Personas**: Who uses this? Researchers? Analysts? Executives? Developers?
2. **Query Complexity**: Simple lookups vs. complex multi-step analysis?
3. **Real-time vs. Batch**: Instant answers vs. long-running analyses?
4. **Trust & Transparency**: Show underlying queries? Confidence scores?
5. **Data Freshness**: Live API calls vs. cached/indexed data?
6. **Scope**: Just your data, or augment with external sources?
---
## Priority Matrix (To Be Filled)
| Capability | Value | Complexity | Priority |
|------------|-------|------------|----------|
| Natural Language Querying | | | |
| Analytical Intelligence | | | |
| Geographic Intelligence | | | |
| Temporal Intelligence | | | |
| Cross-Source Intelligence | | | |
| Knowledge & Context | | | |
| Reporting & Visualization | | | |
| Monitoring & Alerts | | | |
| Research & Discovery | | | |
| ESG & Compliance | | | |
| Conversational Memory | | | |
| Administrative | | | |
---
## Implementation Phases (To Be Defined)
### Phase 1: Foundation
- TBD
### Phase 2: Core Intelligence
- TBD
### Phase 3: Advanced Features
- TBD
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## Technical Considerations
### MCP Server Architecture
- Python-based MCP server
- Leverage existing `eko-client-python` where applicable
- Direct database access for complex queries
- Caching layer for performance
### Integration Points
- Cursor IDE
- Claude Code
- VS Code (via MCP extension)
- Standalone CLI
---
## Changelog
| Date | Change |
|------|--------|
| 2026-01-22 | Initial brainstorm created |
