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llmledger

llmledger

by pouriaetab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Not Yet Priced

not_yet_priced
Read-onlyIdempotent

Lists Arena-rated models missing from LiteLLM's price catalogue and shows how soon each maker's models usually get priced. Use when a new model has no price yet or to see what's coming.

Instructions

List the models Arena has rated that LiteLLM's catalogue does not price yet, with how soon the catalogue usually prices a maker's models after Arena first shows them. Use when a brand-new model is missing from the other tools' answers, or to ask what is coming. Not for models that already have a price (find_models).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "not_yet_priced"
  }
}

Input Schema

NameRequiredDescriptionDefault
makerNoOnly this maker, for example meta or ibm

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 genuine context the annotations cannot: that results include the typical lag between Arena first showing a model and the catalogue pricing it, which shapes how the output should be read. It stops short of describing result ordering or volume, a minor gap given an output schema exists.

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?

Three sentences, all load-bearing, with the definition first and routing second. The opening clause is slightly dense ('models Arena has rated that LiteLLM's catalogue does not price yet') but no sentence is wasted.

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?

For a single optional-parameter read tool with full schema coverage and an output schema, the description covers purpose, routing and the meaning of the returned timing data. The only omission is the behaviour of the default no-maker case.

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 coverage is 100% and the single parameter carries its own description with examples (meta, ibm), so the schema does the heavy lifting. The description implies per-maker aggregation but never says what omitting maker returns (all makers). Baseline 3 is appropriate here.

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?

Opens with a specific verb and a precisely scoped resource: models Arena has rated that LiteLLM's catalogue does not price yet. It also names the sibling (find_models) that covers the complementary set, so an agent can separate the two without reading either schema.

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?

States both triggering conditions (a brand-new model missing from other tools' answers, or asking what is coming) and an explicit exclusion: not for models that already have a price, use find_models. There is essentially nothing left to infer.

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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