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Electricity Maps

Electricitymaps Carbon Intensity

electricitymaps_carbon_intensity
Read-onlyIdempotent

Carbon intensity (gCO2/kWh) for a grid zone now — the live climate cost of a kWh drawn from the local grid. Pass a zone code (e.g. "US-CAL-CISO", "DE", "FR", "GB") or lat+lon. Example: electricitymaps_carbon_intensity({ zone: "DE", _apiKey: "your-key" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude (use with lon instead of zone)
lonNoLongitude (use with lat instead of zone)
zoneNoGrid zone code, e.g. "US-CAL-CISO" (California ISO), "DE" (Germany), "FR" (France), "GB" (Great Britain). Alternatively pass lat + lon.
_apiKeyYesElectricity Maps API key (free trial / developer tier at electricitymaps.com)

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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds useful behavioral context such as 'live'/'now' and the two valid input modes (zone or lat/lon), but does not disclose response format or how recent the live data is.

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?

The description is compact and front-loaded with the core metric and meaning. Every sentence earns its place: the definition, the parameter guidance, and a concrete call example. No filler or redundant information.

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, annotated read-only lookup, the description is nearly complete: it explains the metric, units, input selection, and provides a call example. The only minor gap is the absence of any hint about the response shape, but no output schema exists and the tool is simple enough that this is not a major obstacle.

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 restates the zone/lat/lon selection and provides an example, but it does not add meaningful semantics beyond what the schema already says about each parameter.

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 outcome: it reports the live carbon intensity in gCO2/kWh for a grid zone. It clearly distinguishes this from sibling tools by emphasizing 'now' and 'live', which contrasts with electricitymaps_carbon_forecast, and by naming the metric, which separates it from electricitymaps_power_breakdown.

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 clear instructions on how to call the tool: pass either a zone code or lat/lon, with an example. It does not explicitly state when not to use this tool or point to alternatives, but the 'now' framing implies it is for current conditions rather than forecasts.

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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