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soif-mcp

soif-mcp

by Unchained-Labs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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compare_models

Rank candidate AI models by water footprint for a given workload. Filter by minimum tier and view water use ranges to choose the least-thirsty model.

Instructions

Rank candidate models by mid-scenario water use for a workload.

Returns models sorted least- to most-thirsty, each with its tier and water range in mL. min_tier ("nano"|"small"|"medium"|"large"| "frontier") filters out models below a capability floor.

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": {
      "models": "<models>"
    },
    "name": "compare_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelsYes
min_tierNo
input_tokensNo
output_tokensNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior4/5

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

Since no annotations are provided, the description carries the burden of behavioral disclosure. It explains the return format (sorted list with tier and water range) and the filtering behavior of min_tier. It doesn't mention side effects or permissions, but for a ranking tool, this is reasonably transparent.

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 concise, with two short paragraphs. The first sentence immediately states the primary purpose, and the rest adds essential detail about output and filtering. No unnecessary fluff or redundancy.

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

Completeness2/5

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

The tool has an output schema (though not shown) and a moderately complex set of parameters. The description explains ranking and filtering but omits the role of input_token and output_tokens parameters. It also doesn't clarify what 'models' refers to (e.g., model IDs or names). Given the low parameter coverage and lack of usage guidance, the description is not fully complete.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain parameters. It only clarifies min_tier (with allowed values and meaning) but says nothing about models, input_tokens, or output_tokens. Given that input_tokens and output_tokens have defaults and are likely used in the evaluation, leaving them unexplained is a significant gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool ranks candidate models by mid-scenario water use for a workload. It specifies the output (sorted least- to most-thirsty, with tier and water range in mL). It doesn't explicitly differentiate from sibling tools like pick_low_water_model, but the verb 'rank' and resource 'models' are specific enough.

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?

The description does not provide any guidance on when to use this tool versus alternatives. It states what it does but not when to prefer it over sibling tools. For instance, pick_low_water_model might be for selecting a single model, while this ranks all, but no comparison or exclusion is given.

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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