Data Center Siting Intelligence MCP Server

> View on ApifyForge | Use on Apify Store

---

Quick Start

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

{
  "mcpServers": {
    "data-center-siting-intelligence-mcp": {
      "url": "https://ryanclinton--data-center-siting-intelligence-mcp.apify.actor/mcp"
    }
  }
}

---

Data center site selection intelligence, delivered as an MCP server your AI agent can call directly. This server queries 8 government data sources — USGS seismic catalogs, NOAA weather alerts, OpenAQ air quality, EIA power grid data, FEMA disaster records, UK flood warnings, Nominatim geocoding, and weather forecasts — and synthesizes them through 4 scoring models into a composite Site Viability Score (0-100) for any candidate location. The result is a structured, machine-readable site assessment that replaces days of manual desk research for each shortlisted market.

Data center real estate teams, hyperscaler infrastructure planners, and colocation operators use this server to give their AI assistants the ability to evaluate power availability, natural hazard exposure, PUE cooling potential, and general site viability in a single tool call. Every assessment uses pay-per-event pricing at $0.045 per tool call, with no subscription, no minimum, and no per-seat fee. The same multi-source site screening that commercial advisories charge $15,000-50,000 to produce now costs under $5 for a 100-site national sweep.

What data can you access?

| Data Point | Source | Coverage |
|------------|--------|----------|
| 📊 Seismic activity, earthquake magnitude, M4+ event frequency | USGS Earthquake Search | Global |
| 🌩 Severe weather alerts, storm frequency, extreme event count | NOAA Weather Alerts | US and global |
| 💨 PM2.5 averages, air quality index, free-cooling suitability | OpenAQ Air Quality | 10,000+ global stations |
| 📍 Geographic coordinates, location resolution, geocoding confidence | Nominatim Geocoder | Global |
| 🌡 Temperature profiles, cool-hour ratios, extreme heat periods | Weather Forecast Search | Global |
| ⚡ Electricity generation, fuel mix, renewable percentage, source diversity | EIA Energy Data | US state-level |
| 🌊 Flood warnings, flood zone classifications, flood-related events | UK Flood Warnings | England and Wales |
| 🏚 Federal disaster declarations, disaster type history, frequency | FEMA Disaster Search | All US declarations |

Why use Data Center Siting Intelligence MCP Server?

A site selection analyst manually pulling power data from EIA, cross-referencing USGS earthquake catalogs, checking FEMA disaster histories, and modeling climate conditions spends 8-12 hours per candidate site. At five to ten shortlisted locations, that is a full two weeks of desk research before a single slide is ready for a capital committee.

This MCP server automates the entire data collection and scoring pipeline. Your AI agent calls one tool — site_ranking_matrix — and receives a scored, structured report with a PRIME_SITE / STRONG_CANDIDATE / ACCEPTABLE / MARGINAL / NOT_RECOMMENDED verdict, actionable recommendations, and supporting signals from all 8 sources. The same analysis that takes analysts days takes under 60 seconds.

Beyond speed, deploying through the Apify platform gives your team:

- Scheduling — run daily or weekly site monitoring sweeps to track changing conditions in shortlisted markets
- API access — trigger assessments from Python, JavaScript, or any HTTP client; embed in existing site selection platforms
- Spending controls — set a per-run cost ceiling so batch site surveys stay within budget
- Monitoring — receive Slack or email alerts when runs fail or return unexpected results
- Integrations — connect to Zapier, Make, webhooks, or Google Sheets for stakeholder reporting

Features

- 8 parallel data sources per tool call — site_ranking_matrix fires all 8 underlying actors concurrently via Promise.all, minimizing latency; total latency is determined by the slowest single source, not the sum
- 4 independent scoring models — Power Availability Index, Natural Hazard Composite, Cooling Efficiency Rating, and Site Viability Score each run independently, so partial data still produces valid sub-scores
- Weighted composite formula — composite score weights power availability 30%, natural hazard inversion 25%, cooling efficiency 25%, and site viability 20%, reflecting actual capital planning priorities
- Verdict override logic — EXTREME hazard level or INSUFFICIENT power automatically overrides the composite score to NOT_RECOMMENDED, preventing false-positive site recommendations regardless of other dimensions
- Power grid diversity scoring — EIA series IDs are parsed for solar, wind, hydro, geothermal, nuclear, gas, and coal source types; 5 points per source type up to a 20-point diversity maximum rewards diversified grids
- Renewable energy signal detection — sites where renewable generation exceeds 30% of total EIA output generate an explicit ESG-favorable signal to support PPA targeting and sustainability commitments
- Grid reliability from FEMA disaster history — severe storm, hurricane, ice storm, and winter weather declaration types each deduct 4 points from a 30-point grid reliability baseline
- PUE proxy modeling from cool-hour ratios — cooling score derives from the proportion of forecast periods below 18°C; a 10-point bonus applies for zero extreme heat periods, directly modeling free-cooling hours
- Free-cooling eligibility assessment — PM2.5 averages below 12 µg/m³ score the full 40-point air quality maximum; above 55 µg/m³ generates an explicit air-side economizer warning
- Seismic magnitude weighting — M4.0+ earthquakes each add 5 points to the seismic sub-score; any M5.0+ event adds a flat 10-point penalty regardless of count
- FEMA disaster frequency counter — 5 or more disaster declarations trigger a high-frequency signal; each declaration reduces site viability score by 5 points to a floor of 0
- 8 targeted MCP tools — each tool is scoped to a specific analysis task so AI agents only pay for the data dimensions they need, from focused seismic reports to full composite rankings
- Standby-mode architecture — the server uses Apify's Standby mode with StreamableHTTP transport; each POST to /mcp creates a fresh McpServer instance for stateless, scalable operation
- Structured JSON output with human-readable signals — every tool returns scores, level classifications, signal strings, and actionable recommendation text for direct use in AI reasoning chains

Use cases for data center site selection

Hyperscaler site selection and capital committee reporting

Infrastructure teams at hyperscalers evaluating 10-20 candidate markets for a new AI-focused build can use site_ranking_matrix to generate a scored comparison table in minutes. The structured output maps directly to capital committee templates — composite score, power level, hazard level, cooling level, and specific engineering recommendations. Shortlisting time drops from two weeks of analyst hours to under an hour of API calls.

Colocation operator market expansion

Colo operators identifying their next campus location can use compare_metro_areas to benchmark secondary markets against established hubs like Ashburn, Phoenix, or Dallas. Power mix and cooling efficiency scores reveal which emerging markets have the infrastructure fundamentals to support next-generation AI workloads before committing to site acquisition costs.

Real estate developer due diligence

Real estate developers evaluating land parcels for data center campus development can run natural_hazard_profile and flood_risk_assessment before committing to acquisition. FEMA disaster history and seismic scores inform structural design requirements and insurance underwriting — both material line items in pro forma modeling — before any capital is deployed.

Energy procurement and PPA planning

Energy teams structuring power purchase agreements can use power_grid_analysis to assess the renewable energy mix and source diversity for a given US state before engaging utilities. The renewable indicator signal and energy source type count directly inform PPA target pricing, ESG commitment language, and utility negotiation leverage.

PUE-driven facility engineering

Mechanical and electrical engineers modeling cooling system design can use climate_cooling_efficiency to quantify free-cooling hours and air quality suitability for economizer operation. PM2.5 averages and cool-hour ratios feed directly into PUE projections and total cost of ownership models before schematic design begins.

Insurance underwriting for critical infrastructure

Property insurers underwriting data center facilities can use natural_hazard_profile and seismic_risk_report to quantify multi-peril exposure — earthquake, flood, severe weather, and historical disaster frequency — without pulling data from four separate government databases and normalizing it manually for each submission.

How to use data center site selection MCP tools

1. Connect your MCP client — add the server URL to Claude Desktop, Cursor, Windsurf, Cline, or any MCP-compatible client using the configuration examples below. You only need your Apify API token.
2. Choose your tool — for a full site assessment use site_ranking_matrix; for targeted analysis use individual tools like power_grid_analysis or climate_cooling_efficiency. Most evaluations start with site_ranking_matrix.
3. Provide a location — enter any city name, address, metro area, or geographic region. Example: "Quincy, Washington" or "Northern Virginia, USA". The Nominatim geocoder resolves the location automatically.
4. Receive scored results — within 30-60 seconds the tool returns a structured JSON report with composite score, verdict, sub-scores, key signals, and recommendations ready for stakeholder reporting or downstream pipeline use.

MCP tools

| Tool | Price | Data Sources | Description |
|------|-------|-------------|-------------|
| assess_site_viability | $0.045 | Nominatim, EIA, FEMA, USGS, NOAA | Site Viability Score (0-100) with natural hazard overlay. Returns viability score, hazard score, geocode, and disaster history. |
| compare_metro_areas | $0.045 | EIA, Weather, OpenAQ, FEMA, USGS | Full composite ranking for a metro area: composite score, verdict, power level, hazard level, cooling level, signals, and recommendations. |
| power_grid_analysis | $0.045 | EIA, FEMA | Power Availability Index with energy source diversity, renewable indicators, grid reliability sub-score, and raw EIA generation data. |
| natural_hazard_profile | $0.045 | USGS, NOAA, UK Flood, FEMA | Hazard composite: seismic score, flood score, severe weather score, FEMA disaster count, hazard level classification. |
| climate_cooling_efficiency | $0.045 | OpenAQ, Weather Forecast | Cooling Efficiency Rating: PM2.5 index, cool-hour ratio, temperature score, free-cooling suitability, PUE proxy signals. |
| flood_risk_assessment | $0.045 | UK Flood, FEMA, NOAA | Flood risk score with active flood warnings, FEMA flood-related disaster declarations, and weather-driven flood signals. |
| seismic_risk_report | $0.045 | USGS, Nominatim | Seismic risk score with earthquake catalog slice, magnitude distribution, M4+ event count, max magnitude, and geocoded site location. |
| site_ranking_matrix | $0.045 | All 8 sources | Full site ranking matrix: all 4 scoring models, composite score, verdict (PRIME_SITE through NOT_RECOMMENDED), all signals, and recommendations. |

Connection configuration

Claude Desktop (claude_desktop_config.json):

{

  "mcpServers": {

    "data-center-siting": {

      "type": "streamable-http",

      "url": "https://data-center-siting-intelligence-mcp.apify.actor/mcp",

      "headers": {

        "Authorization": "Bearer YOUR_APIFY_TOKEN"

      }

    }

  }

}

Cursor / Windsurf / Cline:

{

  "mcpServers": {

    "data-center-siting": {

      "url": "https://data-center-siting-intelligence-mcp.apify.actor/mcp?token=YOUR_APIFY_TOKEN"

    }

  }

}

Tool usage tips

- Start with site_ranking_matrix for any new candidate location — it runs all 8 sources in parallel for the same $0.045 as individual tools, giving maximum information per dollar
- Use power_grid_analysis for US regions only — EIA data is US state-level; for non-US power analysis rely on composite signals from weather and disaster data
- Batch candidates with parallel API calls — run site_ranking_matrix on 5-10 locations simultaneously using Promise.all; each call is stateless and independent
- Interpret signals, not just scores — the allSignals and recommendations arrays contain human-readable reasoning behind every score; review these before acting on a verdict
- assess_site_viability vs site_ranking_matrix — assess_site_viability uses 5 sources and returns viability plus hazard sub-scores; site_ranking_matrix runs all 4 models across all 8 sources for the same cost

Output example

The following is example output from site_ranking_matrix for a candidate data center site in Quincy, Washington:

{
  "location": "Quincy, Washington",
  "compositeScore": 78,
  "verdict": "STRONG_CANDIDATE",
  "power": {
    "score": 81,
    "energySources": 4,
    "renewableIndicators": 1,
    "gridReliability": 26,
    "powerLevel": "EXCELLENT",
    "signals": [
      "4 energy source types — diversified power grid",
      "68% renewable energy — ESG-favorable for DC siting"
    ]
  },
  "hazard": {
    "score": 28,
    "seismicRisk": 8,
    "floodRisk": 5,
    "severeWeatherRisk": 9,
    "hazardLevel": "LOW",
    "signals": []
  },
  "cooling": {
    "score": 72,
    "airQualityIndex": 9,
    "temperatureScore": 32,
    "coolingLevel": "GOOD",
    "signals": [
      "Excellent air quality (PM2.5 avg 9.2) — ideal for free cooling",
      "64% cool hours — excellent PUE potential"
    ]
  },
  "siteViability": {
    "score": 75,
    "viabilityLevel": "STRONG",
    "signals": [
      "Location successfully geocoded — accessible site",
      "Strong energy infrastructure data — power grid connectivity likely"
    ]
  },
  "allSignals": [
    "4 energy source types — diversified power grid",
    "68% renewable energy — ESG-favorable for DC siting",
    "Excellent air quality (PM2.5 avg 9.2) — ideal for free cooling",
    "64% cool hours — excellent PUE potential",
    "Location successfully geocoded — accessible site",
    "Strong energy infrastructure data — power grid connectivity likely"
  ],
  "recommendations": [
    "Good cooling conditions — air-side economizers can reduce PUE significantly",
    "Renewable energy available — leverage for PPA and ESG reporting"
  ]
}

Output fields

| Field | Type | Description |
|-------|------|-------------|
| location | string | Input location as provided to the tool |
| compositeScore | number | Weighted composite score 0-100 (power 30%, hazard inverse 25%, cooling 25%, viability 20%) |
| verdict | string | PRIME_SITE / STRONG_CANDIDATE / ACCEPTABLE / MARGINAL / NOT_RECOMMENDED |
| power.score | number | Power Availability Index 0-100 |
| power.energySources | number | Count of distinct energy source types detected (solar, wind, hydro, nuclear, gas, coal) |
| power.renewableIndicators | number | 1 if renewable generation detected in EIA data, 0 otherwise |
| power.gridReliability | number | Grid reliability sub-score 0-30, deducted 4 points per power-disrupting disaster type |
| power.powerLevel | string | EXCELLENT / STRONG / ADEQUATE / CONSTRAINED / INSUFFICIENT |
| power.signals | string[] | Human-readable signals from power availability scoring |
| hazard.score | number | Natural Hazard Composite 0-100 (higher = more hazardous; inverted in composite formula) |
| hazard.seismicRisk | number | Seismic sub-score 0-30 |
| hazard.floodRisk | number | Flood risk sub-score 0-25 |
| hazard.severeWeatherRisk | number | Severe weather sub-score 0-25 |
| hazard.hazardLevel | string | MINIMAL / LOW / MODERATE / HIGH / EXTREME |
| hazard.signals | string[] | Human-readable hazard signals with trigger conditions |
| cooling.score | number | Cooling Efficiency Rating 0-100 |
| cooling.airQualityIndex | number | Average PM2.5 µg/m³ computed from OpenAQ station readings |
| cooling.temperatureScore | number | Temperature sub-score 0-40 based on cool-hour ratio |
| cooling.coolingLevel | string | EXCELLENT / GOOD / ADEQUATE / MARGINAL / POOR |
| cooling.signals | string[] | Free-cooling suitability and PUE-related signals |
| siteViability.score | number | Site Viability sub-score 0-100 |
| siteViability.viabilityLevel | string | PRIME / STRONG / VIABLE / MARGINAL / NOT_VIABLE |
| siteViability.signals | string[] | Geocoding success and energy infrastructure signals |
| allSignals | string[] | Merged signal list from all 4 scoring models |
| recommendations | string[] | Actionable recommendations derived from score threshold conditions |

How much does it cost to evaluate a data center site?

Data Center Siting Intelligence MCP Server uses pay-per-event pricing — each tool call costs $0.045. Platform compute costs are included. There is no subscription, no minimum spend, and no per-seat fee.

| Scenario | Tool Calls | Cost per Call | Total Cost |
|----------|-----------|---------------|------------|
| Single site full assessment | 1 | $0.045 | $0.045 |
| 5-site shortlist comparison | 5 | $0.045 | $0.23 |
| 20-site regional sweep | 20 | $0.045 | $0.90 |
| 100-site national screening | 100 | $0.045 | $4.50 |
| 500-site portfolio assessment | 500 | $0.045 | $22.50 |

You can set a maximum spending limit per run to control costs. The actor stops when your budget is reached, preventing runaway costs on large batch jobs.

Compare this to commercial data center site selection advisory engagements that cost $15,000-50,000 per project or SaaS platforms at $2,000-5,000 per month. A 500-site national screening with this MCP server costs $22.50. Most AI agent workflows spend under $2/month.

Using data center site selection tools via the API

Python

```python
import requests
import json

API_TOKEN = "YOUR_API_TOKEN"

headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_TOKEN}"
}