Climate-Economic Nexus MCP Server

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Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf):

{
  "mcpServers": {
    "climate-economic-nexus-mcp": {
      "url": "https://ryanclinton--climate-economic-nexus-mcp.apify.actor/mcp"
    }
  }
}

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Climate-economic integrated assessment via the Model Context Protocol gives AI agents quantitative tools for scenario analysis, tipping cascade detection, carbon market forecasting, and physical risk attribution. This MCP server orchestrates 18 live environmental and economic data sources — NOAA, GDACS, World Bank, IMF, FRED, GBIF, IUCN, Eurostat, and more — then applies 8 published mathematical frameworks to produce structured, decision-grade outputs.

The server runs persistently on the Apify platform in Standby mode. Any MCP-compatible client — Claude Desktop, Cursor, Windsurf, Cline — connects via a single endpoint URL with no infrastructure to manage. All 8 tools follow pay-per-event pricing: you pay only for the analysis you actually run.

⬇️ What data can you access?

| Data Point | Source | Coverage |
|---|---|---|
| 🌡️ Weather patterns and climate alerts | NOAA Weather Search | US and global |
| 🌊 Sea level and seismic activity | USGS Earthquake Search | Global |
| 🌪️ Real-time disaster events (floods, cyclones, wildfires) | GDACS Disaster Alerts | Worldwide |
| 💨 Air quality measurements | OpenAQ Air Quality | Global monitoring stations |
| 🌧️ Flood risk data | UK Flood Warnings | UK river and coastal systems |
| 🦋 Species occurrence records (2B+ records) | GBIF Biodiversity Search | Global |
| 🔴 Threatened species assessments | IUCN Red List Search | Global species |
| 📊 Country development indicators | World Bank Indicators | 200+ countries |
| 💹 Global macroeconomic projections | IMF Economic Data | Global economies |
| 📈 US economic time series | FRED Economic Data | GDP, CPI, energy |
| 🏛️ OECD member statistics | OECD Statistics Search | OECD nations |
| 🇪🇺 EU environmental and economic stats | Eurostat Data Search | EU member states |
| 💱 Live exchange rates (150+ currencies) | Exchange Rate Tracker | Global |
| 📉 Historical exchange rate series | Exchange Rate History | Time series |
| 📍 Geographic coordinate resolution | Nominatim Geocoder | Global |
| 🏚️ US disaster declarations | FEMA Disaster Search | All US events |
| 🌍 Climate adaptation projects | World Bank Projects Search | Global |
| 🌤️ Multi-day weather forecasts | Weather Forecast Search | Global locations |

Why use Climate-Economic Nexus MCP?

Building your own climate-economic analytics pipeline means licensing data from NOAA, World Bank, IUCN, and a dozen other sources, implementing published damage functions, running Monte Carlo simulations, and maintaining all of it. That is months of engineering work — and it still doesn't give you integrated cross-source analysis.

This MCP server handles the entire data collection and computation layer. Your AI agent calls a single tool with a natural-language query, and the server fans out to all 18 data sources in parallel, calibrates the mathematical models from live data, and returns structured JSON results ready for decision support.

Platform capabilities your agent inherits automatically:

- Standby mode — the server stays warm between calls, so there is no cold-start latency on inference requests
- Parallel execution — all 18 actor calls run concurrently via Promise.all, reducing per-tool latency to the slowest single source
- Spending limits — set a maximum budget per session; all tools check the limit before charging
- API access — connect from any MCP client or trigger analysis directly from Python, JavaScript, or cURL
- Monitoring — Apify platform logging captures every data source call, result count, and error for debugging
- Integrations — output can be piped to Zapier, Make, Google Sheets, webhooks, or HubSpot via Apify integrations

⬆️ MCP Tools

| Tool | Price | Description |
|---|---|---|
| simulate_integrated_assessment | $0.035 | DICE optimal control: three-reservoir carbon cycle, two-box energy balance, discounted utility maximization. Returns SCC, peak warming, year of 2°C, carbon budget, optimal carbon tax path. Queries 18 actors. |
| detect_tipping_cascades | $0.030 | Cusp catastrophe bifurcation analysis of AMOC, Amazon, ice sheets, coral reefs, and permafrost as coupled SDEs with Heaviside coupling. Returns cascade probability, tipped count, system risk, element-level bifurcation states. |
| quantify_damage_uncertainty | $0.030 | Bayesian hierarchical damage model D(T) = a1·T + a2·T² with Gibbs-like posterior updates. Returns regional damage estimates with 95th/99th percentile tail risk, model disagreement score. |
| optimize_robust_adaptation | $0.040 | PRIM bump-hunting in scenario space + MOEA Pareto optimization. Returns adaptation strategies on cost-robustness-regret Pareto front, vulnerable scenario boxes, strategy rankings. |
| downscale_spatial_impacts | $0.035 | Gaussian process spatial downscaling with Matern-3/2 kernel. Maps temperature anomalies, precipitation, sea level, and damage intensity at configurable grid resolution. |
| forecast_carbon_price_regimes | $0.030 | Regime-switching jump-diffusion with Hamilton filter. Identifies low-stable, policy-transition, and high-volatile carbon price regimes. Returns transition matrix, forecast 90% CI, jump probability. |
| assess_biodiversity_economic_loss | $0.030 | Species-area relationship S = cA^z with percolation theory on habitat lattice. Quantifies ecosystem service economic losses per region and identifies habitat connectivity collapse thresholds. |
| attribute_climate_damages | $0.030 | Optimal fingerprinting via Total Least Squares. Decomposes observed damages into anthropogenic and natural variability components. Returns attribution fraction, detection statistic, signal betas. |

Use cases for climate-economic integrated assessment

Climate risk financial disclosure

Chief Risk Officers and sustainability teams building TCFD-aligned disclosures need quantified scenario analysis under RCP 2.6, 4.5, and 8.5 pathways. Use simulate_integrated_assessment for physical risk trajectories and downscale_spatial_impacts to map temperature and damage exposure at asset-level granularity. quantify_damage_uncertainty provides the confidence intervals required for robust disclosure narratives.

Carbon market strategy and ETS trading

Carbon trading desks and corporate sustainability teams managing emissions obligations need to anticipate price regime transitions before they happen. forecast_carbon_price_regimes applies regime-switching jump-diffusion with Hamilton filter smoothing to identify when the EU ETS or other markets are approaching a policy-transition regime, including the 90% confidence interval for price in each regime.

Insurance catastrophe modeling and loss-and-damage assessment

Actuaries pricing climate-related property insurance and reinsurance need to separate the anthropogenic climate signal from natural variability in historical loss data. attribute_climate_damages uses optimal fingerprinting with Total Least Squares — the same framework used in Allen and Stott (2003) — to compute what fraction of observed damages is attributable to anthropogenic forcing, supporting defensible actuarial assumptions and climate litigation analysis.

Adaptation investment planning under deep uncertainty

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