keyfo

List models

list_models

Read-onlyIdempotent

The AI models a keyfo key can call: price per million tokens, context length and what each supports (tools, reasoning, vision, json). Filter by what the task needs; a trial key sees its low-cost list. Listing is free.

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
`can`	No	What the model must support, comma-separated: tools, reasoning, vision, json. Empty for any.
`sort`	No	price (cheapest first, the default), context (longest first) or name.	price
`limit`	No	How many to return, from 1 to 100. 20 by default.
`query`	No	A word in the model ID or name, like gemini or grok.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and closed-world scope, so the safety profile is fully covered. The description adds genuinely useful context beyond that: cost-free listing and the trial-key subset behavior. It does not mention pagination behavior or whether limit caps the total, but the added context is real.

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

Conciseness4/5

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

Three short sentences, front-loaded with what the tool returns and what the data includes; nothing is padded. The filter sentence is slightly clipped ('Filter by what the task needs') and the trial-key note is folded in abruptly, but overall it is dense and purposeful.

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 read-only four-parameter listing tool with full schema coverage and no output schema, the description covers what is returned, the filtering intent, and the trial-key caveat. Missing only minor operational details such as whether limit truncates the full catalog silently.

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 coverage is 100% and each parameter already documents its format, defaults, and enum values, so the schema does the heavy lifting. The description reinforces the intent of the filter ('Filter by what the task needs') but adds no syntax or edge-case detail beyond the schema, making the baseline 3 correct.

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 verb+resource (listing AI models available to a keyfo key) and enumerates the fields returned: price per million tokens, context length, and capability flags. It is distinguishable from siblings like model_detail (single model) and ask_model (invocation) without opening a schema.

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?

It gives clear usage context — 'Filter by what the task needs' — and notes that a trial key sees a restricted low-cost list, plus that listing is free. It does not explicitly name an alternative tool or a when-not-to-use case, but the intended pre-selection workflow is clear.

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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