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Get Relay pricing

get_pricing
Read-onlyIdempotent

Retrieves Relay's USD list prices for AI models and media storage retention fees—optionally scoped to one model or your account tier—so you can estimate costs accurately.

Instructions

Returns Relay's list prices (USD), optionally for one model (with a credential your account's view: its tier and grants; public otherwise), plus media_storage: the per-file fees of the store_output / upload retention SKUs (relay1d, relay7d, …). Then estimate_cost for a number.

Input Schema

NameRequiredDescriptionDefault
modelNoModel name (e.g. google/gemini-nano-banana-2.1) to keep only its rows. Omit for every model.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.8

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent and open-world traits, so the bar is lower, and the description still adds real context: results differ depending on whether a credential is supplied (account tier/grants view vs public list prices) and it names the media_storage section of the response. It does not describe response shape in detail or ordering, keeping it below 5.

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

Conciseness3/5

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

Front-loaded with the main purpose in the first clause, which is good. However, the nested parenthetical 'with a credential your account's view: its tier and grants; public otherwise' is dense and awkward to parse, and the final fragment 'Then estimate_cost for a number' is clipped. Information-dense but not cleanly structured.

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?

No output schema exists, so the description carries the return-shape burden and does reasonably well: it names USD list prices, per-model rows, and the media_storage per-file fee section with example SKU names. What is missing is any indication of row structure or volume for the no-model case, which matters for an open-world read.

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?

With one optional parameter at 100% schema coverage, the schema already documents the model filter including the 'omit for every model' behavior. The description adds the nuance that credentialed calls return the account's own tier/grants pricing, but that is about the call context rather than the parameter itself. Baseline 3 is appropriate.

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 verb and resource ('Returns Relay's list prices (USD)') and enumerates the payload sections (per-model rows, media_storage per-file fees for retention SKUs). It also names the sibling it is not ('Then estimate_cost for a number'), so an agent can distinguish it from the cost-calculation tool. The parenthetical about credentialed vs public views muddies it slightly but the core purpose is unmistakable.

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

Usage Guidelines4/5

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

Explicitly routes the agent: call this to get price data, then call estimate_cost to turn it into a number. It also states the model-omission behavior ('Omit for every model'), which is a usage rule. No explicit when-not beyond that, so it stops short of a 5.

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