llm-price-mcp

list_models

Lists LLMs and their per-million-token API prices from OpenRouter, with optional provider or keyword filtering and a result limit for quick price comparison.

Instructions

List LLMs and their prices (USD per 1M tokens) from OpenRouter.

Args: filter: Optional provider or keyword, e.g. "anthropic", "gpt", "deepseek coder". All space-separated terms must match the model id/name. limit: Max number of models to return (default 50).

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
`limit`	No
`filter`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Nov 3, 2026

TDQS

A3.7/5.0

Behavior3/5

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

With no annotations provided, the description carries the behavioral burden and does add useful context: the data source is OpenRouter, prices are in USD per 1M tokens, and filter terms must all match the model id/name. However, it does not explicitly state read-only behavior, ordering of results, or any rate/auth constraints.

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 front-loaded with the core purpose, followed by a compact Args section. Every sentence contributes useful information, and there is no redundant or filler wording.

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?

The tool is simple, has an output schema, and the description fully covers both input parameters and the pricing unit/source. The main completeness gap is the lack of sibling-routing guidance and any note about result ordering or read-only behavior in the absence of annotations.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must explain both parameters, and it does: filter is an optional provider or keyword with examples and matching semantics, and limit is the max number of models with a default of 50. This adds substantial meaning beyond the raw schema.

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: listing LLMs and their prices in USD per 1M tokens from OpenRouter. It is clear what the tool does, but it does not explicitly distinguish itself from siblings such as cheapest or compare_cost.

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 provides no guidance on when to use this tool versus alternatives like cheapest or compare_cost. It documents arguments but does not state exclusions, prerequisites, or routing context for an agent choosing among the sibling tools.

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

Deploy Server