eco-router-mcp

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Rank cloud regions by carbon

rank_regions

Read-only

Rank AWS, GCP, and Azure regions by grid carbon intensity, optionally weighted by latency. Filter by country, latency, and carbon limits to find the lowest-emission region for a workload.

Instructions

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.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	How many regions to return. Default 5.
`origin`	No	Where users or data are. Enables latency estimates and latency weighting.
`countries`	No	Only consider regions in these countries or groups (EU, EEA), e.g. ["EU"] for EU data residency. Default: all.
`energyKwh`	No	Estimated job energy, to report emissions per region.
`providers`	No	Only consider these cloud providers. Default: all.
`carbonWeight`	No	Weight on carbon versus latency, 0..1. Default 0.7. Only used with origin.
`maxLatencyMs`	No	Exclude regions estimated slower than this round trip. Requires origin.
`maxCarbonIntensity`	No	Exclude regions above this carbon intensity, gCO2e/kWh.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`notes`	Yes
`results`	Yes
`excluded`	Yes
`evaluated`	Yes
`qualified`	Yes
`generatedAt`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 17, 2026

TDQS

A4.1/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds behavior beyond the readOnlyHint annotation: the ranking is based on electricity-grid carbon intensity, latency is 'estimated' from an origin, and the filters are 'hard limits'. The warning to 'Read notes before relying on close scores' is a valuable behavioral caveat.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The primary purpose and criterion are front-loaded in the first sentence, and the second sentence adds constraint types and a specific caveat. Every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter ranking tool with 100% schema coverage and an output schema, the description covers providers, ranking basis, optional latency, constraint types, and the main caveat. It does not need to document every parameter because the schema already does so.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description summarizes hard limits for countries, latency, and carbon intensity, but does not add detail beyond what each parameter schema already states. It does not repeat or undermine the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Rank'), a concrete resource ('AWS, Google Cloud and Azure regions'), and a clear criterion (carbon intensity, optionally balanced against latency). This clearly differentiates it from sibling tools like list_regions and find_clean_window.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives useful context: ranking is for 'a workload' with optional latency balancing and hard constraints. However, it does not explicitly state when to prefer this tool over its siblings, nor when not to use it, so the guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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