AICostBudget AI API Pricing

List models

list_models

Read-onlyIdempotent

Retrieve source-linked AI models and filter by provider or maximum input/output token prices to compare API costs before choosing a model.

Instructions

List source-linked models, optionally filtering by provider and maximum token prices.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_models"
  }
}

Input Schema

Name	Required	Description	Default
`provider`	No
`max_input_price`	No
`max_output_price`	No

Output Schema

Name	Required	Description	Default
`data`	Yes
`source`	Yes
`verified_at`	Yes
`aicostbudget_url`	Yes
`bundled_fallback`	Yes
`official_source_urls`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 21, 2026

TDQS

B3.4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the agent knows this is a safe, repeatable read operation. The description adds only that models are 'source-linked' and filters are optional; it does not add other behavioral details such as pagination or result ordering, so a baseline 3 is appropriate given the annotation coverage.

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?

The description is a single front-loaded sentence with no wasted words. It immediately conveys the core action and the optional filter behavior, making it appropriately sized for a simple list tool.

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?

For a simple list operation with rich annotations and an output schema, the description is largely complete. It states the core purpose and optional filters; remaining gaps like provider enumeration and price units are minor because the output schema and structured annotations cover the safety and return-shape concerns.

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 0%, so the description must compensate. It names the filter dimensions (provider and maximum token prices) and implies that both price parameters relate to token pricing, but it does not clarify accepted provider values, whether prices are input/output-specific, units, or currency. It adds some meaning over the bare schema but remains incomplete.

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?

The description states a specific verb and resource: 'List source-linked models,' with optional filtering by provider and maximum token prices. It is clear enough to distinguish from siblings like compare_models or get_model_price, but it does not explicitly name an alternative or state how listing differs from those tools.

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?

The description says the tool can optionally filter by provider and maximum token prices, but it gives no when-to-use guidance, no conditions for choosing this over compare_models, get_model_price, price_changes, or estimate_monthly_cost, and no exclusions. Usage context is left entirely to inference.

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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