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llmledger

llmledger

by pouriaetab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Compare Models

compare_models
Read-onlyIdempotent

Compare 2 to 6 named models for a specific kind of work: Arena rating, prices or plan usage speed, cost per task, the top pick, and each model's token share needed to win. Use when you already have candidates.

Instructions

Compare 2 to 6 named models for a kind of work: rating, prices, cost per task (or how fast each uses a Claude or ChatGPT plan), the pick, and for each model the share of answer tokens at which it would beat the pick. Use when the user already has candidate models in mind. Not for finding candidates (find_models), for reading a prompt to guess the work (suggest_for_project), or for a monthly budget (estimate_cost).

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
payNoapi: dollars; claude or chatgpt: how fast each uses that planapi
taskNoThe kind of work, as Arena rates itoverall
modelsYes2 to 6 model names, for example claude-opus-5.5, gpt-6.1-sol
input_tokensNoPrompt tokens per task
answer_tokensNoAnswer tokens per model when they differ, by the names given
output_tokensNoAnswer tokens per task, including hidden thinking

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.7

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context by explaining that 'pay' switches the meaning of the numbers between dollars and plan consumption, and that a per-model breakeven token share is returned. It does not say what happens when a supplied model name is unknown or unpriced (there is a not_yet_priced sibling), which is the main remaining gap.

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

Conciseness4/5

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

Two sentences, front-loaded with the compare action and the returned fields, then the routing rules. The first sentence is long and comma-heavy but every clause carries information; there is no filler.

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?

With an output schema present the description need not restate return values, and annotations cover the read-only/idempotent profile, so the description's job is mainly routing and it does that well. The only shortfall is guidance on unpriced or unrecognized model names, which matters given a sizable model-name surface.

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 every parameter including the pay enum and the answer_tokens-per-model map is already documented. The description adds the 2-6 model bound and the dollars-vs-plan distinction, but this largely restates what the schema states. Baseline 3 applies when the schema does the heavy lifting.

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 (compare) and resource (models), bounds the scope to 2-6 named models, and enumerates the outputs the agent will get back: rating, prices, cost per task, the pick, and each model's breakeven token share. This is clearly separable from every sibling at a glance.

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?

Gives an explicit inclusion condition ('when the user already has candidate models in mind') and then names three exclusions with the sibling that handles each: find_models for discovering candidates, suggest_for_project for inferring work from a prompt, estimate_cost for monthly budget. Nothing is left to inference.

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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