model-price-radar

estimate_cost

Read-onlyIdempotent

Estimate LLM workload costs from input/output tokens and call volume across multiple models at current prices, showing lower-cost options first.

Instructions

Cost of a workload on several models at today's prices, cheapest first.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`calls`	No	Number of calls, e.g. per day or per month.
`models`	Yes	Model ids or names, e.g. ['openai/gpt-4o-mini', 'claude haiku'].
`input_tokens`	Yes	Input tokens per call.
`output_tokens`	Yes	Output tokens per call.
`response_format`	No	'markdown' (default) or 'json'.	markdown

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 1, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already cover read-only, idempotent, non-destructive, open-world. The description adds the useful result-ordering trait (cheapest first) and signals current pricing, but says nothing about currency, whether figures are estimates vs billed rates, or how model-name resolution failures behave.

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?

One line, front-loaded with the core action and scope, with the ordering detail appended. Nothing 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?

Given an output schema exists and schema coverage is total, the description need not explain return values. It conveys the essential framing (today's prices, cheapest first) for a 5-param tool; only edge-case behavior is unaddressed.

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 'calls', token counts, and response_format is already documented. The description adds no syntax or semantics beyond what the schema provides, which is the baseline-3 case.

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?

States a specific action (estimate cost) and resource (a workload across several models), plus the ordering (cheapest first). It is distinguishable from find_models and price_changes, though it never names them.

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

Usage Guidelines3/5

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

The phrase 'at today's prices' implicitly contrasts with sibling tools like model_price_history and price_changes, so usage context is inferable. But there is no explicit when-to-use statement, no exclusions, and no routing to alternatives.

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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