Climate Financial Stranded Asset MCP Server

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Quick Start

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

{
  "mcpServers": {
    "climate-financial-stranded-asset-mcp": {
      "url": "https://ryanclinton--climate-financial-stranded-asset-mcp.apify.actor/mcp"
    }
  }
}

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Climate financial stranded asset analysis for AI agents — this MCP server gives any LLM client direct access to eight quantitative climate risk tools covering carbon asset stranding, portfolio climate VaR, physical hazard exposure, biodiversity-financial coupling, transition contagion, and full TCFD reporting. It is built for investment analysts, risk teams, and sustainability professionals who need institutional-grade climate metrics without constructing the data infrastructure themselves.

The server orchestrates 14 live data sources — SEC EDGAR, FRED, World Bank, OECD, Eurostat, IMF, OpenAQ, IUCN Red List, GBIF, UN COMTRADE, OpenCorporates, GLEIF, Nominatim, and SEC insider filings — and applies six quantitative algorithms to produce TCFD-aligned output. Connect once via the Model Context Protocol and every supported AI client can call all eight tools without additional setup.

What data can you extract?

| Data Point | Source | Example |
|---|---|---|
| 📊 Carbon asset stranding probability | Markov regime-switching Monte Carlo | XOM: 73% stranding prob, $2.1B expected loss |
| 📉 Portfolio climate VaR at 99% confidence | Monte Carlo (10,000 paths) + GPD tails | $485M VaR99, $612M Expected Shortfall |
| 🏭 Physical hazard exposure by facility | Spatial lat/lng overlay + OpenAQ | Midland TX refinery: 34% composite risk score |
| 🌿 Biodiversity-financial risk (TNFD) | IUCN Red List + GBIF occurrence trends | Agriculture co: "high" TNFD category, 12 VU species |
| 🔗 Transition contagion amplification factor | Climate-adjusted DebtRank propagation | 3.2x amplification across 8-node exposure network |
| 📐 Emissions trajectory alignment score | Dynamic Time Warping vs IEA/IPCC pathways | Alignment score 0.41, gap years: 2026, 2027, 2029 |
| 💨 Scope 1/2/3 carbon exposure analysis | SEC EDGAR + sector benchmarks | 850 tCO2e/$M vs sector avg 720, 18% above benchmark |
| 📋 TCFD findings across 4 governance pillars | Aggregated from all tools | 3 critical findings, overall risk rating: "high" |
| 🌐 SEC corporate climate filings | SEC EDGAR + SEC insider filing analyzer | Climate disclosures, emissions data per company |
| 📡 Real-time air quality monitoring (PM2.5, AQI) | OpenAQ | Live AQI at facility coordinates (200km radius) |
| 🐾 Biodiversity threat assessments | IUCN Red List | VU/EN/CR species status and GBIF population trends |
| 📈 Carbon pricing policy data | OECD, World Bank, Eurostat | EU ETS price series, carbon tax schedules |
| 🏢 Corporate registry and legal entity data | OpenCorporates + GLEIF LEI | Subsidiary mapping for financial exposure networks |
| 📉 Macro economic indicators | FRED, IMF, World Bank | Oil price series (WTI), energy CPI, GDP data |

Why use the Climate Financial Stranded Asset MCP Server?

Building climate risk quantification from scratch requires sourcing data from a dozen APIs, implementing actuarial models, and maintaining the pipeline as the underlying data changes. A single stranded asset analysis for a 10-stock portfolio can take a senior analyst two to three days using Excel and point-in-time data. Getting to TCFD-compliant output typically requires an expensive vendor — MSCI Climate, Sustainalytics, or S&P Trucost — or months of internal model development costing well over $50,000 annually.

This MCP server reduces that to a single tool call. An AI agent connected via MCP can generate a full TCFD report, run Monte Carlo climate VaR, identify stranding probabilities, and map physical hazard exposure in one conversation — using the same algorithms institutional risk desks rely on.

- Scheduling — run climate risk assessments daily, quarterly, or on custom intervals to track portfolio exposure as policy signals evolve
- API access — trigger tool calls from Python, JavaScript, Claude Desktop, Cursor, or any MCP-compatible HTTP client
- 14 live data sources — all underlying actor calls use fresh public API data with no stale cache
- Monitoring — get Slack or email alerts when runs fail or produce unexpected results via Apify platform integrations
- Integrations — connect output to Zapier, Make, Google Sheets, or webhooks to feed downstream risk workflows

Features

- Carbon asset stranding probability via real options with Markov regime switching — models asset values under two carbon price regimes (current policy: $30–$70/tCO2e over 21 years; net zero: $30–$600/tCO2e) with a Markov transition matrix (8% annual probability of transitioning to net zero regime, 3% reversion probability), running up to 5,000 Monte Carlo paths to derive book-value-weighted stranding probabilities and early-divestment real option values per asset
- Monte Carlo portfolio climate VaR with Generalized Pareto Distribution tail fitting — runs up to 10,000 simulations across three correlated risk factors (carbon price shock volatility 0.4, energy cost shock 0.25, regulatory shock 0.15), fits GPD tails using method-of-moments estimation above the 95th-percentile threshold, and reports VaR and Expected Shortfall at 95%, 99%, and 99.5% confidence levels
- Spatial physical hazard overlay across 7 hazard types — overlays earthquake (Ring of Fire and tectonic boundaries), flood (coastal/river proximity by latitude/longitude zone), wildfire (30–45° and 10–25° dry latitude bands), hurricane (tropical warm ocean basins), drought, sea level rise, and live air quality (OpenAQ PM2.5 within 200km via Haversine distance) against facility coordinates; composite risk = sum(hazard_probability × asset_value × vulnerability_factor)
- TNFD biodiversity-financial risk coupling for 10 ecosystem services — scores pollination, water purification, soil formation, climate regulation, flood protection, carbon sequestration, nutrient cycling, pest control, raw materials, and genetic resources using sector-level dependency intensity tables, IUCN Red List threat scores (LC=0.1 to EX=1.0), and GBIF population trend data; produces TNFD risk categories (negligible/low/moderate/high/very_high) per entity
- Climate-adjusted DebtRank for systemic transition contagion — propagates carbon price shocks through a user-defined financial exposure network; initial shock = carbonIntensity × carbonPriceIncrease × fossilRevenueShare; stress propagates with climate correlation adjustment (entities sharing high carbon intensity amplify each other's distress); returns amplification factor, per-node climate debt rank, and iterations to convergence
- Dynamic Time Warping emissions trajectory alignment — compares normalized company emissions trajectories (first year indexed to 100) against IEA Net Zero (reaching 0 by 2050) or IPCC 1.5C reference pathways; DTW distance computed via D(i,j) = cost(i,j) + min(D(i-1,j), D(i,j-1), D(i-1,j-1)); alignment score = 1/(1 + DTW_distance/path_length); identifies gap years where entity emissions exceed reference by more than 20%
- Sector carbon intensity benchmarking across 14 sectors — energy: 850, oil_gas: 950, utilities: 720, mining: 600, chemicals: 380, transportation: 310, agriculture: 290, materials: 480, industrials: 220, real estate: 95, consumer staples: 120, healthcare: 45, technology: 30, communication services: 20 tCO2e/$M revenue
- TCFD-structured reporting across 4 pillars — aggregates all analysis dimensions into governance, strategy, risk management, and metrics/targets findings with severity ratings (low/medium/high/critical) and quantitative recommendations
- Seeded Monte Carlo for reproducibility — uses a linear congruential generator (seed default: 42) with Box-Muller normal sampling so simulation results are reproducible across runs with identical inputs
- Parallel actor orchestration across 14 data sources — underlying calls to SEC EDGAR, World Bank, FRED, IUCN, GBIF, OpenAQ, and other sources run concurrently via Promise.all, minimizing total latency
- Standby mode (always-on MCP endpoint) — runs in Apify Standby mode at the /mcp endpoint, eliminating cold-start latency for interactive agent sessions

Use cases for climate financial stranded asset analysis

Investment portfolio stress testing

Asset managers and risk officers need to quantify how transition scenarios affect portfolio value before regulatory stress tests or internal risk reviews. Connect this MCP server to your analysis workflow, pass in a list of equity positions with their carbon intensity and fossil revenue share, and receive Monte Carlo VaR figures at 95%, 99%, and 99.5% confidence alongside per-asset stranding probabilities — all calibrated to IEA and IPCC carbon price pathways. A full 10-asset analysis runs in under two minutes.

TCFD and SFDR climate disclosure

Sustainability teams preparing TCFD Task Force disclosures or SFDR Article 8/9 fund reporting need structured climate risk findings across all four TCFD pillars. The generate_tcfd_report tool aggregates stranding probability, climate VaR, physical risk, biodiversity exposure, and emissions alignment into a single TCFD-structured report with severity ratings and actionable recommendations — giving sustainability teams a documented, quantitative starting point for board-level review.

Physical asset and infrastructure risk mapping

Infrastructure investors, insurers, and corporate risk teams assessing physical climate exposure can geocode facility addresses or pass coordinates directly to assess_physical_risk and receive multi-hazard composite risk scores overlaid with live air quality data. The tool identifies the dominant hazard type across a portfolio and ranks facilities by composite expected loss — useful for prioritizing adaptation capital expenditure.

Net-zero alignment and shareholder engagement

ESG research firms and engagement teams tracking corporate emissions trajectories against science-based targets can use score_transition_alignment to compare historical emissions series against IEA Net Zero or IPCC 1.5C reference pathways using Dynamic Time Warping distance. The output identifies specific gap years where an entity's trajectory deviates more than 20% from the reference, providing a defensible alignment score for shareholder engagement letters and proxy voting decisions.

Systemic climate risk and financial contagion modelling

Central banks, financial stability boards, and academic researchers modelling systemic transition risk can use model_transition_contagion to propagate a carbon price shock through a financial exposure network using the climate-adjusted DebtRank algorithm. Input the network of entity financial exposures, specify the carbon price increase, and receive amplification factors, per-node stress levels, and convergence data — output consistent with FSB and BIS climate stress-test methodologies.

Biodiversity and nature risk for TNFD reporting

Companies preparing Taskforce on Nature-related Financial Disclosures reports need to quantify their dependencies on ecosystem services. The compute_biodiversity_risk tool pulls IUCN Red List threat status and GBIF species occurrence trends for the geography of operations, scores 10 ecosystem service dependencies by sector, and assigns a TNFD risk category — giving sustainability teams a defensible, data-backed starting point for nature-related disclosure.

How to use the climate financial stranded asset MCP server

1. Connect to your AI client — add the MCP server URL https://climate-financial-stranded-asset-mcp.apify.actor/mcp to Claude Desktop, Cursor, or any MCP-compatible client using the JSON config below. No code is required.
2. Describe your portfolio — tell your AI agent which companies to analyze, their sectors, and what carbon intensity data you have. For physical risk, include facility addresses or coordinates. The agent calls the appropriate tools automatically.
3. Run the analysis — the agent calls the relevant tools in sequence or parallel. A full TCFD report for a 10-asset portfolio typically takes 60–120 seconds. A single carbon exposure analysis returns in under 15 seconds.
4. Review and export results — output is structured JSON returned directly in the conversation. Ask the agent to format findings as a table, extract specific metrics, or push results to a downstream system via webhook.

Connect via Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "climate-stranded-asset": {
      "type": "http",
      "url": "https://climate-financial-stranded-asset-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Connect via Cursor or any MCP client

Use the endpoint https://climate-financial-stranded-asset-mcp.apify.actor/mcp with a Bearer token header containing your Apify API token.

Input parameters

This is an MCP server with no traditional actor input. All parameters are passed as tool call arguments by the AI agent. The server exposes 8 tools:

| Tool | Key Parameters | Description |
|---|---|---|
| compute_stranding_probability | assets[], discountRate, numPaths | Array of AssetPosition objects. Each requires ticker, name, sector, bookValue, marketValue, carbonIntensity (tCO2e/$M revenue), fossilRevenueShare (0–1), annualEmissions (tCO2e). Optional: lat, lng, jurisdiction. Default: discountRate=0.05, numPaths=5000. |
| simulate_climate_var | assets[], numSimulations, carbonPriceVolatility, energyCostVolatility, regulatoryVolatility | Same AssetPosition array. numSimulations default 10,000. Volatility defaults: carbon 0.4, energy 0.25, regulatory 0.15. |
| assess_physical_risk | facilities[] | Array of facility objects: name, lat, lng, assetValue (USD), facilityType. Provide address instead of coordinates to trigger Nominatim geocoding. |
| compute_biodiversity_risk | entities[], species[], region | Each entity requires name, sector, revenue, locations[] (lat/lng array). species and region are optional — defaults to pollinator, mangrove, coral, and forest queries. |
| model_transition_contagion | entities[], carbonPriceIncrease | Each entity requires name, carbonIntensity, fossilRevenueShare, economicValue, exposures[] (target + amount). carbonPriceIncrease in USD/tCO2e, default 100. |
| score_transition_alignment | entities[], referencePathway | Each entity requires name and emissionsHistory[] (array of {year, scope1, scope2, scope3?}). referencePathway: "IEA_NZ" or "IPCC_15C". |
| analyze_carbon_exposure | assets[], carbonPrice | Same AssetPosition array. carbonPrice in USD/tCO2e for cost calculation, default 50. |
| generate_tcfd_report | assets[], facilities[], emissionsHistory[], carbonPrice, carbonPriceIncrease | Full portfolio data. Runs all six algorithms internally and aggregates findings across all four TCFD pillars. |

AssetPosition schema (used by 4 tools)

| Field | Type | Required | Description |
|---|---|---|---|
| ticker | string | Yes | Stock ticker symbol (e.g. "CVX") |
| name | string | Yes | Company name |
| sector | string | Yes | Sector key — see 14-sector benchmark table in Features |
| bookValue | number | Yes | Book value in USD |
| marketValue | number | Yes | Market value in USD |
| carbonIntensity | number | Yes | tCO2e per $M revenue |
| fossilRevenueShare | number | Yes | Fraction of revenue from fossil fuels (0–1) |
| annualEmissions | number | Yes | Total annual emissions in tCO2e |
| lat | number | No | Facility latitude for physical risk overlay |
| lng | number | No | Facility longitude |
| jurisdiction | string | No | Regulatory jurisdiction (e.g. "US", "EU") |

Input examples

Single asset stranding probability check:

{

  "assets": [

    {

      "ticker": "CVX",

      "name": "Chevron Corporation",

      "sector": "oil_gas",

      "bookValue": 156000000000,

      "marketValue": 295000000000,

      "carbonIntensity": 920,

      "fossilRevenueShare": 0.95,

      "annualEmissions": 58000000,

      "lat": 37.8,

      "lng": -122.3,

      "jurisdiction": "US"

    }

  ],

  "discountRate": 0.05,

  "numPaths": 5000

}

Portfolio climate VaR with two assets and custom volatility:

{

  "assets": [

    {

      "ticker": "NEE",

      "name": "NextEra Energy",

      "sector": "utilities",

      "bookValue": 42000000000,

      "marketValue": 110000000000,

      "carbonIntensity": 280,

      "fossilRevenueShare": 0.35,

      "annualEmissions": 8200000

    },

    {

      "ticker": "RIO",

      "name": "Rio Tinto",

      "sector": "mining",

      "bookValue": 32000000000,

      "marketValue": 98000000000,

      "carbonIntensity": 580,

      "fossilRevenueShare": 0.4,

      "annualEmissions": 27000000

    }

  ],

  "numSimulations": 10000,

  "carbonPriceVolatility": 0.45,

  "energyCostVolatility": 0.30,

  "regulatoryVolatility": 0.20

}

Physical risk for three facilities across different geographies:

{

  "facilities": [

    {

      "name": "Permian Basin Refinery",

      "lat": 31.8,

      "lng": -102.4,

      "assetValue": 2400000000,

      "facilityType": "refinery"

    },

    {

      "name": "Gulf Coast LNG Terminal",

      "lat": 29.7,

      "lng": -93.8,

      "assetValue": 890000000,

      "facilityType": "lng_terminal"

    },

    {

      "name": "Rotterdam Petrochemical Plant",

      "lat": 51.9,

      "lng": 4.4,

      "assetValue": 1100000000,

      "facilityType": "petrochemical"

    }

  ]

}

Input tips

- Use market value, not book value, for carbon intensity calibration — the model assumes a 12% revenue yield on market value; carbon intensity expressed as tCO2e per $M revenue should match that scale
- Provide lat/lng directly for physical risk accuracy — the spatial hazard overlay produces more reliable composite risk scores when exact coordinates are provided rather than relying on Nominatim geocoding from a text address
- Pass all portfolio assets in a single call — Monte Carlo climate VaR is more accurate at portfolio level when all positions are processed together, as correlated shocks are modelled across the whole portfolio simultaneously
- Use sector names that match the benchmark table — recognized sectors include energy, utilities, materials, industrials, oil_gas, mining, chemicals, transportation, agriculture, financials, technology, healthcare, real_estate, and communication_services; unrecognized sectors fall back to the default benchmark (150 tCO2e/$M revenue)
- For TCFD reports, use generate_tcfd_report as a single call — it runs all six algorithms internally; no need to call individual tools first

Output example

Full TCFD report output for a mixed-sector portfolio:

{
  "tool": "generate_tcfd_report",
  "result": {
    "overallClimateRisk": "high",
    "climateRiskScore": 72,
    "strandingProbability": 0.61,
    "climateVaR99": 487200000,
    "physicalRisk": 143800000,
    "transitionRisk": 0.68,
    "alignmentScore": 0.38,
    "findings": [
      {
        "category": "strategy",
        "subcategory": "transition_risk",
        "finding": "Portfolio stranding probability of 61% exceeds the 50% high-risk threshold. CVX and XOM contribute 78% of total expected loss under net-zero transition.",
        "severity": "critical",
        "recommendation": "Reduce fossil revenue share exposure by 15–20% through divestment of highest-stranding-probability assets. Redeploy capital into positions with fossilRevenueShare < 0.2."
      },
      {
        "category": "metrics_targets",
        "subcategory": "carbon_exposure",
        "finding": "Portfolio weighted-average carbon intensity of 685 tCO2e/$M revenue is 2.9x the sector peer median of 238. Scope 3 emissions represent an estimated 67% of total footprint.",
        "severity": "high",
        "recommendation": "Set Scope 3 reduction targets aligned with SBTi FLAG methodology. Engage top 5 suppliers representing 43% of Scope 3 on emissions reduction roadmaps."
      },
      {
        "category": "risk_management",
        "subcategory": "physical_risk",
        "finding": "Gulf Coast LNG Terminal faces composite physical risk of 38.4% of asset value driven by hurricane (18.2%) and sea level rise (12.1%) hazards. OpenAQ AQI of 142 adds regulatory exposure.",
        "severity": "high",
        "recommendation": "Commission site-specific resilience assessment for Gulf Coast terminal. Model 1m and 2m sea level rise scenarios against current flood barrier specifications."
      },
      {
        "category": "governance",
        "subcategory": "disclosure",
        "finding": "Emissions trajectory alignment score of 0.38 indicates significant divergence from IEA Net Zero pathway. Gap years identified: 2026, 2027, 2029, 2031.",
        "severity": "medium",
        "recommendation": "Establish board-level climate risk committee with quarterly reporting cadence. Integrate DTW alignment score into executive compensation scorecard."
      }
    ],
    "recommendations": [
      "Divest assets with strandingProbability > 0.65 within 18 months to reduce transition risk",
      "Implement climate VaR limit of $400M at 99% confidence; current exposure $487M exceeds limit",
      "Begin TNFD nature-related disclosure for agriculture and mining portfolio companies in FY2026",
      "Set internal carbon price of $80/tCO2e for capital allocation decisions"
    ]
  }
}

Output fields

| Field | Type | Description |
|---|---|---|
| overallClimateRisk | string | TCFD aggregate risk rating: low, medium, high, or critical |
| climateRiskScore | number | Composite score 0–100 across all climate dimensions |
| strandingProbability | number | Portfolio book-value-weighted stranding probability (0–1) |
| climateVaR99 | number | Portfolio climate Value at Risk at 99% confidence (USD) |
| physicalRisk | number | Total physical risk expected loss across all facilities (USD) |
| transitionRisk | number | Normalized transition risk score (0–1) |
| alignmentScore | number | DTW-based emissions alignment score (0–1; 1.0 = fully aligned with reference pathway) |
| findings[].category | string | TCFD pillar: governance, strategy, risk_management, or metrics_targets |
| findings[].severity | string | Finding severity: low, medium, high, or critical |
| findings[].finding | string | Narrative finding with specific quantitative metrics |
| findings[].recommendation | string | Actionable recommendation with quantitative targets |
| recommendations[] | string[] | Top-level portfolio-wide priority recommendations |
| assets[].ticker | string | Asset identifier |
| assets[].strandingProbability | number | Per-asset stranding probability from Monte Carlo regime switching (0–1) |
| assets[].expectedLoss | number | Expected loss under regime-switching model (USD) |
| assets[].optionValue | number | Real option value of early divestment (USD) |
| assets[].currentPolicyValue | number | DCF value under current policy carbon prices (USD) |
| assets[].netZeroValue | number | DCF value under net-zero carbon price path $30–$600 (USD) |
| vaR.confidenceLevel | number | Confidence level (0.95, 0.99, or 0.995) |
| vaR.climateVaR | number | Climate VaR at specified confidence level (USD) |
| vaR.expectedShortfall | number | Expected Shortfall (CVaR) at specified confidence level (USD) |
| vaR.gpdShape | number | Generalized Pareto Distribution shape parameter xi |
| vaR.gpdScale | number | GPD scale parameter sigma |
| vaR.monteCarloPathCount | number | Number of Monte Carlo simulation paths run |
| vaR.worstCaseLoss | number | Worst single simulated portfolio loss (USD) |
| facilities[].compositeRisk | number | Sum of expected losses across all hazard types (USD) |
| facilities[].hazards[].hazardType | string | One of: earthquake, flood, wildfire, hurricane, drought, air_quality, sea_level_rise |
| facilities[].hazards[].probability | number | Annual exceedance probability for the hazard type |
| facilities[].hazards[].vulnerabilityFactor | number | Asset vulnerability multiplier (0.2–0.7 depending on hazard) |
| facilities[].hazards[].expectedLoss | number | probability × assetValue × vulnerabilityFactor (USD) |
| biodiversity[].tnfdCategory | string | TNFD risk tier: negligible, low, moderate, high, or very_high |
| biodiversity[].dependencies[].service | string | Ecosystem service (e.g. pollination, water_purification, carbon_sequestration) |
| biodiversity[].dependencies[].dependencyIntensity | number | Sector-level dependency on this ecosystem service (0–1) |
| biodiversity[].dependencies[].financialRisk | number | Financial risk from this dependency (USD) |
| biodiversity[].iucnThreatCount | number | Count of VU/EN/CR/EW-status species in the analysis |
| biodiversity[].speciesAtRisk | number | Count of GBIF species with declining population trend (< -0.2) |
| contagion.systemicClimateRisk | number | Climate DebtRank = sum(stress_i × economic_value_weight_i) |
| contagion.amplificationFactor | number | Ratio of systemic risk to initial shock magnitude |
| contagion.maxDebtRank | number | Highest single-entity climate debt rank in the network |
| contagion.iterations | number | DebtRank propagation iterations to convergence |
| alignment[].dtwDistance | number | Raw DTW distance between entity trajectory and reference pathway |
| alignment[].alignmentScore | number | Normalized score 0–1; 1/(1 + DTW_distance/path_length) |
| alignment[].gapYears[] | number[] | Years where entity emissions exceed reference by more than 20% |
| carbonExposure[].carbonCost | number | Annual carbon cost at the given carbon price (USD) |
| carbonExposure[].revenueAtRisk | number | Revenue at risk from carbon cost as fraction of total revenue |
| carbonExposure[].benchmarkIntensity | number | Sector benchmark carbon intensity (tCO2e/$M revenue) |
| carbonExposure[].relativeIntensity | number | Entity intensity relative to sector benchmark (ratio) |

How much does it cost to run climate stranded asset analysis?

This MCP server uses pay-per-event pricing — you pay per tool call. Platform compute costs are included in the price. There is no subscription or monthly minimum.

| Tool | Price per call | Typical calls per session |
|---|---|---|
| compute_stranding_probability | $0.060 | 1–5 (per batch of assets) |
| simulate_climate_var | $0.065 | 1–3 |
| assess_physical_risk | $0.055 | 1–5 (per facility batch) |
| compute_biodiversity_risk | $0.050 | 1–3 |
| model_transition_contagion | $0.060 | 1–2 |
| score_transition_alignment | $0.050 | 1–3 |
| analyze_carbon_exposure | $0.055 | 1–5 |
| generate_tcfd_report | $0.065 | 1 |

Scenario cost estimates:

| Scenario | Tool calls | Estimated cost |
|---|---|---|
| Single asset stranding check | 1 | $0.060 |
| Full portfolio VaR + stranding (10 assets) | 2 | $0.125 |
| Physical risk mapping (5 facilities) | 1 | $0.055 |
| Complete TCFD report (all tools) | 1 | $0.065 |
| Daily automated risk monitoring | 8–12 per day | $0.50–$0.75/day |
| Monthly institutional risk review | ~50 calls | ~$2.75–$3.25 |

You can set a maximum spending limit per run in Apify to control costs. The server stops accepting new tool calls when your spending limit is reached and returns a Spending limit reached message for the blocked call.

Compare this to MSCI Climate Risk, Sustainalytics, or S&P Trucost at $20,000–$100,000+ per year — with this MCP server, most teams spend under $10/month for comparable quantitative analytics without a vendor contract.

Use the climate financial stranded asset MCP server via API

Python

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")