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green-router__quote_footprint

Calculate the energy and carbon footprint of an agent workload. Input workload parameters to get detailed emissions and offset data.

Instructions

[green-router — carbon-accounted agent compute: free footprint quotes + carbon routing; certificates = real verified offset retirement] FREE: honest energy/carbon footprint for an agent workload — {backend_id? (frontier_cloud | efficient_cloud | local_small), total_tokens, output_tokens, calls?, success_score?, grid_gco2e_per_kwh?, pue?, custom_backend? {id, wh_per_1k_tokens}}. Returns joules, gCO2e, useful bits, the retirement mass a certificate would carry, every model assumption with its source, and the Landauer context (GR1/GR2).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workloadYes
Behavior3/5

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

The description explains what the tool returns (joules, gCO2e, assumptions, etc.) and lists possible input fields. Since no annotations are provided, it carries the full burden, but it does not explicitly state that it is read-only or any side effects. It adds some behavioral context but is not exhaustive.

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

Conciseness3/5

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

The description is a single run-on sentence that mixes purpose, parameter details, and return values. While it front-loads the purpose, the inline parameter list reduces readability. More structured formatting would improve conciseness.

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?

Given the flexible input schema and no output schema, the description adequately explains input expectations and return values, including assumptions and Landauer context. It is sufficient for an agent to use the tool correctly.

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

Parameters5/5

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

The input schema is minimal (object with additionalProperties), so the description's enumeration of possible fields (backend_id, total_tokens, etc.) provides crucial semantics that the schema lacks. This fully compensates for the 0% schema coverage.

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 that the tool provides a free energy/carbon footprint quote for an agent workload. It specifies the action (quote) and the resource (footprint of workload), distinguishing it from sibling tools like listing certificates.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives (e.g., neurogenesis__compute_efficiency_report). The description does not mention any circumstances where this tool is appropriate or not.

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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