Rank AWS, Google Cloud and Azure regions by grid carbon intensity and latency.
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io.github.stevensu04/eco-router-mcp is an MCP server that rank AWS, Google Cloud and Azure regions by grid carbon intensity and latency. Its tool list has not been published yet over stdio, requires no API key, and scores 82/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"eco-router": {
"env": {
"ELECTRICITY_MAPS_API_TOKEN": "optional"
},
"args": [
"-y",
"eco-router-mcp"
],
"command": "npx"
}
}
}Are you the author?
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Rank AWS, Google Cloud and Azure regions by grid carbon intensity and latency.
Run this in your terminal to verify the server starts. Then let us know if it worked - your result helps other developers.
npx -y 'eco-router-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"
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An MCP server that helps AI agents pick the lowest-carbon cloud region for a workload.
The same GPU job can emit several times more CO₂ depending on which grid powers the datacenter. Eco Router maps AWS, Google Cloud and Azure regions to the electricity grids they draw from, so an agent can ask:
"Where should I run this training job? Data must stay in the EU and latency should be under 150 ms."
and get back a ranked shortlist with the reasoning behind it.
Carbon figures are location-based grid averages. They describe the grid that supplies each region, not the renewable energy a provider buys, so regions in the same grid get the same value. See what the numbers represent.
Status: early release (v0.1). Corrections and feedback are welcome in issues. See Roadmap.
Requires Node.js 22 or later.
Claude Code
claude mcp add eco-router -- npx -y eco-router-mcp
Claude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"eco-router": {
"command": "npx",
"args": ["-y", "eco-router-mcp"],
"env": { "ELECTRICITY_MAPS_API_TOKEN": "optional" }
}
}
}
Out of the box, Eco Router uses annual averages (per grid in the US, per
province in Canada, per country elsewhere), so it works with no setup. For hourly, grid-level data, set ELECTRICITY_MAPS_API_TOKEN to your
own Electricity Maps API token. Zones your
plan does not cover fall back to the annual average automatically.
Your use of live data is governed by your own Electricity Maps plan and its terms. At the time of writing, Electricity Maps offers free access for academic and personal non-commercial use, and a 14-day trial for commercial evaluation. Commercial use requires a paid license. Eco Router does not provide or share any token.
| Tool | What it does |
|---|---|
rank_regions | Ranks regions by grid carbon intensity, optionally balanced against estimated latency from an origin. Supports providers, countries, maxLatencyMs, maxCarbonIntensity, carbonWeight, energyKwh and limit. |
find_clean_window | For flexible batch jobs, finds the start time in the next 72 hours with the lowest forecast carbon intensity in each grid zone, and the savings compared with starting now. Requires ELECTRICITY_MAPS_API_TOKEN with forecast access. |
list_regions | Lists cloud regions and the grid zone each one draws power from. Filter by provider (aws, gcp, azure) or country. |
countries accepts ISO 3166-1 alpha-2 codes and the groups EU and EEA.
Example request to rank_regions:
{
"countries": ["EU"],
"origin": { "lat": 50.11, "lon": 8.68 },
"maxLatencyMs": 40,
"energyKwh": 500
}
Example request to find_clean_window for a 6-hour job that must finish within
two days:
{
"countries": ["DE", "FR", "SE"],
"durationHours": 6,
"withinHours": 48,
"energyKwh": 500,
"timezone": "Australia/Brisbane"
}
Scoring, data sources and their limits are explained in docs/methodology.md.
How regions are mapped to grids, and which mappings rest on assumptions, is documented in docs/regions.md.
rank_regions: rank regions by carbon intensity and estimated latency,
with hard limits such as allowed countries or a carbon ceiling