eco-router-mcp
Provides carbon-aware ranking of Google Cloud regions, enabling selection of low-carbon regions based on grid carbon intensity and optional latency constraints.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@eco-router-mcp Rank EU regions for a GPU job, latency under 100ms."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Eco Router MCP
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.
Install
Requires Node.js 22 or later.
Claude Code
claude mcp add eco-router -- npx -y eco-router-mcpClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"eco-router": {
"command": "npx",
"args": ["-y", "eco-router-mcp"],
"env": { "ELECTRICITY_MAPS_API_TOKEN": "optional" }
}
}
}Live grid data (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.
Related MCP server: Tidegap
Tools
Tool | What it does |
| Ranks regions by grid carbon intensity, optionally balanced against estimated latency from an |
| 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 |
| Lists cloud regions and the grid zone each one draws power from. Filter by |
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.
Roadmap
MCP server skeleton over stdio
Full AWS, Google Cloud and Azure region dataset with sources
Carbon data: published annual averages by default, live data with an optional Electricity Maps API token
rank_regions: rank regions by carbon intensity and estimated latency, with hard limits such as allowed countries or a carbon ceilingUS grid-level annual data without a token (EPA eGRID)
Canadian provincial annual data without a token (National Inventory Report)
Grid-level annual data for Australia
Time shifting: suggest when to run, using carbon forecasts
Publish to npm and the MCP Registry
Development
npm install
npm test # unit tests, no network needed
npm run typecheck
npm run build # compiles to dist/
npm run dev # runs the server over stdio from source
npm run update:baseline # refreshes national averages from Ember
npm run update:egrid # refreshes US grid averages from EPA eGRID
npm run update:canada # refreshes Canadian provincial averagesData sources
Annual carbon intensity: Ember, Yearly Electricity Data, licensed under CC BY 4.0.
US grid generation mix: US EPA eGRID (public domain), converted to lifecycle emissions with Ember's US factors.
Canadian provincial generation mix: Environment and Climate Change Canada, National Inventory Report, Annex 7, converted to lifecycle emissions with Ember's Canadian factors. Contains information licensed under the Open Government Licence – Canada.
Live carbon intensity (optional): Source: ElectricityMaps.com, using your own API token and subject to the terms of your plan.
Region lists: official AWS, Google Cloud and Azure documentation. See docs/regions.md.
Contributing
Corrections to region data are especially welcome. See CONTRIBUTING.md to get started.
Credits
Eco Router started as a hackathon project. Thanks to the original team for the scoring design this project builds on: Chris (@chrislee021225), Joli (@L-Joli), Bob (@Loic0927), Irene Tsai (@YunTong09) and Steven Su (@stevensu04).
License
Available Tools
3 toolsfind_clean_windowFind the cleanest time to run a jobARead-only
For flexible batch jobs, find when in the next hours (up to 72) each candidate region's grid is forecast to be cleanest, and how much that saves compared with starting now. Local times default to this computer's time zone. Needs ELECTRICITY_MAPS_API_TOKEN with forecast access. Limit candidates with regions, providers or countries (at most 15 grid zones per call).
Inputs: durationHours (required), limit, regions, timezone, +4 more. Returns: notes, results, timezone, generatedAt, +6 more
list_regionsList cloud regionsARead-onlyIdempotent
List the cloud regions Eco Router knows about, with the electricity grid zone each one draws power from.
Inputs: country, provider. Returns: count, regions
rank_regionsRank cloud regions by carbonARead-only
Rank AWS, Google Cloud and Azure regions for a workload by the carbon intensity of their electricity grid, optionally balanced against estimated latency from an origin. Supports hard limits for allowed countries, maximum latency and maximum carbon intensity. Read notes before relying on close scores.
Inputs: limit, origin, countries, energyKwh, +4 more. Returns: notes, results, excluded, evaluated, +2 more
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 3 tools
Each tool has a distinct purpose: listing known regions, ranking regions for a workload, and finding optimal start times for batch jobs. There is no overlap in what they return or when an agent would choose one over another.
All three tool names follow a consistent verb_noun pattern: list_regions, rank_regions, find_clean_window. The verbs clearly indicate the action and the nouns indicate the object.
Three tools is on the low end, but the server has a focused purpose: carbon-aware region selection. Each tool covers a distinct stage of that workflow, so the count feels appropriate rather than thin.
The tool surface covers the core workflow: discover regions, rank them for a workload, and find clean windows for flexible jobs. A minor gap is the lack of a tool to fetch detailed grid data for a single region, but agents can work around it with list_regions and rank_regions.
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