TopxAI Models

List models

list_models

Read-only

List the text models this API key can ask, with their USD prices per million tokens, the reasoning efforts each takes and the call fee.

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
No arguments

Output Schema

Name	Required	Description	Default
`models`	No
`call_fee_usd`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedNov 8, 2026

addedOutput schema / properties / models / items / properties / backup_cache_read_usd_per_million_tokens

Added value: +{
+  "description": "The cache-read price of a call the backup deployment answers.",
+  "type": "number"
+}

addedOutput schema / properties / models / items / properties / backup_cache_write_usd_per_million_tokens

Added value: +{
+  "description": "Only when the backup deployment bills cache writes: the cache-write price of a call it answers.",
+  "type": "number"
+}

addedOutput schema / properties / models / items / properties / backup_input_usd_per_million_tokens

Added value: +{
+  "description": "Only on a model with a backup deployment: the input price of a call the backup deployment answers, which happens only if the main deployment fails before answering.",
+  "type": "number"
+}

addedOutput schema / properties / models / items / properties / backup_output_usd_per_million_tokens

Added value: +{
+  "description": "The output price of a call the backup deployment answers.",
+  "type": "number"
+}

First observedNov 6, 2026

TDQS

A4/5.0

Behavior3/5

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

readOnlyHint=true already tells the agent this is a safe, side-effect-free read, so the description's job is lighter. It usefully ties results to the calling API key, implying the list is authorization-scoped, but says nothing about whether the list is exhaustive, cacheable, paginated, or how often pricing changes — all of which matter when an agent uses prices to decide.

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?

One sentence, front-loaded with the verb and resource, then the returned fields in priority order (model, price, reasoning effort, fee). No preamble or redundant restatement of the name and title.

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?

A zero-parameter read tool with an output schema and a readOnly annotation needs little prose, and the description covers purpose and payload adequately. A small gap remains around whether only text models are listed and how 'call fee' relates to the per-token prices, which an agent needs to interpret the output correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly implies the call is unconditional and unfiltered.

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?

States a specific verb and resource ('List the text models') plus the exact scope — models this API key can call — and enumerates what comes back (USD price per million tokens, reasoning efforts, call fee). Nothing about that is confusable with the sibling tools ask_model or judge, which are inference operations, not enumeration.

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?

Usage is only implied: an agent can infer this is the discovery step before calling ask_model, since it returns prices and supported reasoning efforts. There is no explicit when-to-use statement, no note about calling it before ask_model to pick a model or effort level, and no exclusions.

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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