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by whiteknightonhorse
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TypeScript
Remote

UK Carbon Intensity — 24-Hour Forecast

carbonintensity.energy.forecast
Read-onlyIdempotent

Fetches UK National Grid's 24-hour carbon intensity forecast with half-hour gCO2/kWh periods, helping schedule EV charging or energy-intensive workloads for lower-carbon windows.

Instructions

Get the 24-hour ahead forecast of UK national carbon intensity from the National Grid ESO. Returns up to 48 half-hour periods (fully covering the next 24 hours), each with a forecast intensity in gCO2/kWh and a qualitative index (very low / low / moderate / high / very high). Use the optional periods parameter to limit results (e.g. periods=6 for 3 hours ahead). Ideal for scheduling carbon-intensive workloads, EV charging, or energy storage dispatch at the lowest-carbon future window. CC BY 4.0, no upstream cost.

Input Schema

NameRequiredDescriptionDefault
periodsNoNumber of 30-minute forecast periods to return (1–48, default: all 48 covering 24 hours)

Output Schema

NameRequiredDescriptionDefault
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.2.0

TDQS

A4.5/5.0
Behavior4/5

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

Beyond the read-only and idempotent annotations, the description adds valuable context: the return format (48 half-hour periods, intensity in gCO2/kWh, qualitative index categories), the optional periods parameter, and licensing/cost details ('CC BY 4.0, no upstream cost'). This exceeds what annotations alone convey.

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?

Five concise sentences, each serving a distinct purpose: purpose, return format, parameter example, use case, and licensing. Front-loaded with the core action and scope, no redundant or filler content.

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 a single-optional-parameter read tool with an output schema, the description fully covers purpose, return semantics, parameter usage, and cost/licensing. Nothing necessary for correct invocation is missing.

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

Parameters4/5

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

Schema coverage is 100% and the schema already explains the periods parameter, but the description adds a concrete example ('periods=6 for 3 hours ahead') that clarifies the mapping from periods to time and encourages correct usage.

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?

States a specific verb ('Get'), resource ('24-hour ahead forecast of UK national carbon intensity'), and source ('National Grid ESO'). This clearly differentiates it from sibling tools like carbonintensity.energy.current or regional by emphasizing the forecast horizon and national scope.

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?

Provides clear context with 'Ideal for scheduling carbon-intensive workloads, EV charging, or energy storage dispatch at the lowest-carbon future window.' It does not explicitly name alternatives or exclusions, but the intended use case is unambiguous and sufficient for a simple read tool.

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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