easiapiai - LLM API prices

Monthly cost of a workload

estimate_cost

Read-onlyIdempotent

Monthly USD cost of a workload (tokens per request, requests per day, share of input served from cache) for 1-10 models, each at its cheapest standard seller, sorted cheapest first, with the official list price for comparison. With one model it also prices every seller.

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>",
      "input_tokens": "<input_tokens>",
      "output_tokens": "<output_tokens>",
      "requests_per_day": "<requests_per_day>"
    },
    "name": "estimate_cost"
  }
}

Input Schema

Name	Required	Description
`models`	Yes	Model names or ids.
`cached_share`	No	Share of input tokens served from the prompt cache, 0-1. Default 0.
`input_tokens`	Yes	Input tokens per request.
`output_tokens`	Yes	Output tokens per request (include reasoning tokens if the model thinks).
`requests_per_day`	Yes	Requests per day; a month is 30 days.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 19, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint=false), so the bar is lower. The description adds real behavioral context beyond that: pricing is at each model's cheapest standard seller, results are sorted cheapest first, the official list price is included for comparison, and with a single model every seller is priced.

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?

Two sentences, no filler, with the core computation front-loaded and the single-model edge case appended last. Every clause carries information an agent would otherwise have to guess.

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 no output schema, the description carries the return-value burden and does so adequately: sorted order, cheapest standard seller, list-price comparison, and the single-model all-seller behavior. It stops short of specifying the response shape or what cost components are included, but it is sufficient to call the tool correctly.

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 the schema already documents all five parameters, making 3 the baseline. The description restates cached_share and the 1-10 model limit but adds no format or syntax detail the schema lacks, so it does not earn above baseline.

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?

The description names a specific computation (monthly USD cost of a workload) and enumerates the exact inputs it consumes (tokens per request, requests per day, cache share) for 1-10 models. It is clearly distinct from the sibling get_model_prices, since this returns workload cost with a list-price comparison rather than raw prices.

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 scenario is implied by the input enumeration (you have a request volume and want monthly cost), but there is no explicit when-to-use statement, no exclusions, and no named alternative such as get_model_prices for raw pricing. Usage is inferable rather than stated.

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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