Skip to main content
Glama

Get one model's full pricing

get_model_price

Fetch complete pricing for a given LLM model, including standard, cached input, cache write, batch, long-context surcharge, future price changes, and the provider source page.

Instructions

Full pricing for one model: standard, cached input, cache write, batch, long-context surcharge, announced future price changes and the provider's source page.

Input Schema

NameRequiredDescriptionDefault
modelYesAPI model ID or name, e.g. gpt-6.1-terra or Claude Sonnet 5.5

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description bears the full burden; 'full pricing for one model' implies a read-only lookup, but nothing states that it does not mutate anything, how an unknown or non-existent model ID is handled, or how fresh the prices are. It does add real value by listing exactly which price categories come back, which substitutes for the missing output schema.

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?

A single front-loaded sentence with no filler; the itemized list is dense but each element conveys a distinct returned price category. Slight cost in readability from packing seven items into one clause.

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

Completeness3/5

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

For a one-parameter lookup with no output schema, the description does the main job of telling the agent what data comes back. It omits units/currency, per-token vs per-million normalization, and error behavior, which matter for a pricing tool an agent will feed into cost calculations.

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?

Only one parameter exists and schema coverage is 100%, with the schema itself giving format examples ('gpt-6.1-terra or Claude Sonnet 5.5'). The description adds no matching or normalization rules beyond the schema, so baseline 3 applies.

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?

Names a specific resource (full pricing) with a clear scope restriction (one model) and enumerates the price dimensions returned, so an agent can distinguish it from list_models and compare_models without opening the schema. It never names a sibling explicitly, which keeps it just short of a 5.

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?

There is no when-to-use or when-not-to-use guidance and no mention of alternatives such as estimate_cost (which computes a spend figure) or compare_models (multi-model). That the tool is for a single model's detail is only inferable from the phrasing 'one model'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.