GreenCalculus

calculate_electricity

Calculate GHG Protocol Scope 2 emissions from purchased electricity. Input consumption and grid factor to get location-based emissions; add a supplier or market factor for market-based results.

Instructions

GHG Protocol Scope 2 for purchased electricity, both methods. Always returns location-based (grid-average) emissions; also returns market-based when you supply a contractual supplier_factor (e.g. a green tariff / REC = 0) or a market_factor_key (residual mix). Find grid keys via search_factors (section "grid").

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "consumption": "<consumption>",
      "location_factor_key": "<location_factor_key>"
    },
    "name": "calculate_electricity"
  }
}

Input Schema

Name	Required	Description
`consumption`	Yes	{ "value": <number>, "unit": "kWh\|MWh\|GWh" }.
`supplier_factor`	No	Optional contractual factor { value, unit } (wins over market_factor_key).
`market_factor_key`	No	Optional: residual-mix / supplier grid factor key.
`location_factor_key`	Yes	Grid-average factor key, e.g. grid.gbr.electricity.location_based.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 21, 2026

TDQS

A4.6/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It transparently discloses that location-based is always returned and market-based is conditional, and even gives an example (green tariff/REC=0). However, it doesn't explicitly state precedence behavior (supplier_factor wins over market_factor_key), which is only in the schema description.

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 three sentences, front-loaded with the core purpose, then covering behavior, alternatives, and a pointer to a related tool. Every sentence adds value with no redundancy or fluff.

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?

The tool has 4 parameters, no output schema, and no annotations; the description covers the purpose, behavior, and how to source factor keys. It lacks explicit details about output format, but that isn't required given the absence of an output schema. It adequately addresses the complexity of the tool.

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%, so baseline is 3. The description adds meaningful context by explaining that location_factor_key is grid-average, market_factor_key is residual-mix, and supplier_factor can be a contractual value like a green tariff/REC=0. This goes beyond simply repeating parameter names.

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 clearly states the tool calculates GHG Protocol Scope 2 for purchased electricity and explicitly identifies both methods (location-based and market-based). It distinguishes itself from sibling tools like calculate_activity and calculate_embodied by specifying the exact resource and metric.

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

Usage Guidelines5/5

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

The description gives explicit guidance on when market-based results are available (when supplier_factor or market_factor_key is supplied) and clarifies that location-based is always returned. It also directs users to search_factors for finding grid keys, providing a clear path to obtaining necessary inputs.

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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