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ZerouterAI

zerouter-mcp

Official
by ZerouterAI

Get Zerouter pricing

get_pricing
Read-only

Fetch per-million-token pricing for AI models from Zerouter in USD, with optional filters by model or provider. No API key required.

Instructions

Public per-model prices on Zerouter in USD per 1M tokens (input/output), next to the official list price. No API key needed. Data comes from https://www.zerouter.ai/pricing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoFilter by model ID or name substring, e.g. 'claude' or 'gpt-6'
providerNoFilter by provider: anthropic, openai, gemini, grok, kimi

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, yet the description adds genuinely useful context: no authentication required, the exact data source URL, and the unit/currency (USD per 1M tokens, input/output). This is more than a restatement of the annotations.

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 tightly written sentences with zero filler; the core payload (what data, unit, comparison) is front-loaded, and the source URL is relegated to a trailing clause.

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 by stating units, currency, and the list-price comparison. Missing only details like whether results are paginated or how freshness is determined, which are minor for a small read-only catalog call.

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% with two optional filters ('model' substring, 'provider'), so the schema already carries full parameter meaning. The description adds nothing about filtering syntax or behavior, which is the expected baseline when the schema does the heavy lifting.

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 the resource precisely: per-model prices in USD per 1M tokens, with input/output and a comparison to the official list price. An agent can distinguish it from list_models or compare_models, though the description never names those siblings explicitly to sharpen the boundary.

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?

No statement of when to use this versus list_models or compare_models. 'No API key needed' is a usage fact but not a selection rule, and no prerequisites, exclusions, or trigger conditions are given.

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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