@true402.dev/mcp-server

list_models

Retrieve all available LLM models with live per-token pricing across providers. Enables agents to select a model and know exact cost before paying via x402.

Instructions

List every LLM model available through true402's pay-per-call inference, with live per-token pricing (3% over provider cost, min $0.0001/request). Returns each model's id, provider, and input/output token prices across OpenAI, Anthropic, Google, Groq, Mistral and Together — so an agent can pick a model and know the exact cost before paying via x402 (USDC on Base, no account or API key).

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

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.1Jun 20, 2026

TDQS

A4.3/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses return fields (id, provider, prices), pricing model (3% over cost, min fee), and payment method (x402). Missing details on pagination or limits, but acceptable for a listing tool.

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?

Single paragraph, front-loaded with purpose. Concise but could be slightly shorter. No wasted sentences.

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, but description details return values (id, provider, prices). Covers essential behavior. No obvious gaps given zero parameters.

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?

Input schema has 0 parameters and schema description coverage is 100%. Description does not need to add param info; baseline 4 applies.

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 clearly states the tool lists LLM models with live pricing, using specific verbs ('list', 'returns'). It distinguishes from sibling tools (e.g., chat, address_safety) which have unrelated purposes.

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?

Implied usage: when an agent needs to pick a model and know cost before paying. No explicit when-not or alternatives, but sibling tools are unrelated, so no confusion.

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