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Electricity Maps

Electricitymaps Carbon Forecast

electricitymaps_carbon_forecast
Read-onlyIdempotent

Carbon-intensity forecast for a zone — upcoming hourly gCO2/kWh predictions, useful for scheduling loads when the grid is cleanest. Requires a paid Electricity Maps plan. Pass a zone code (e.g. "US-CAL-CISO", "DE", "FR", "GB"). Example: electricitymaps_carbon_forecast({ zone: "GB", _apiKey: "your-key" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zoneYesGrid zone code, e.g. "US-CAL-CISO" (California ISO), "DE" (Germany), "FR" (France), "GB" (Great Britain). Alternatively pass lat + lon.
_apiKeyYesElectricity Maps API key (forecast requires a paid plan)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already disclose readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds the paid-plan requirement and the temporal granularity of the data, but it does not describe the return format, pagination, or error behavior. This is adequate but not rich behavioral context.

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?

Three tight sentences: purpose, prerequisite, and parameter guidance, followed by a concrete example. No filler, and the most important information is front-loaded.

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

Completeness4/5

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

For a simple two-parameter, read-only forecast tool, the description covers purpose, use case, required plan, and invocation. It does not discuss edge cases such as invalid zone codes or the exact JSON response shape, but the schema and annotations carry most of the structured burden, and the output is summarized well enough.

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 coverage is 100%, with both 'zone' and '_apiKey' fully described and accompanied by examples. The description repeats zone examples and provides a call example, but it does not add materially new meaning beyond the schema, so the baseline 3 is appropriate.

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 states a specific operation and resource: producing a carbon-intensity forecast for a zone, with the metric and horizon explicitly named ('upcoming hourly gCO2/kWh predictions'). This makes it clearly distinguishable from the current-intensity sibling, electricitymaps_carbon_intensity, and from the power-breakdown tool.

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

Usage Guidelines4/5

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

It gives a concrete use case ('useful for scheduling loads when the grid is cleanest') and a hard prerequisite ('Requires a paid Electricity Maps plan'). It does not explicitly name the alternative tool for current carbon intensity or state when not to use the forecast, so it stops short of full routing guidance.

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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