llm-price-mcp

gpu_rental_prices

Compare GPU rental prices per GPU-hour across providers, filtered by GPU model or provider, to find the lowest-cost option for training or inference.

Instructions

GPU rental prices per GPU-hour (RunPod, Lambda, Vast.ai, AutoDL, ...), cheapest first.

Data comes from the newest price-tracker CSV (data//gpu_prices.csv), or the file in the LLM_PRICE_GPU_CSV env var, falling back to a dated snapshot bundled with the package. Each row carries its source URL and fetch date; CNY prices are also converted to USD.

Args: gpu: Optional GPU name filter, e.g. "H100", "a100 sxm", "4090" (all terms must match). provider: Optional provider filter, e.g. "runpod", "lambda", "vast", "autodl".

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": "gpu_rental_prices"
  }
}

Input Schema

Name	Required	Description	Default
`gpu`	No
`provider`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Nov 3, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Since no annotations exist, the description is highly transparent, detailing crucial behavioral aspects like data source reliability, file paths, and currency conversion logic (CNY to USD).

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?

The structure is logical, moving from general purpose to specific data reliability, though the necessity of detailing the CSV source makes it slightly less concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

It is extremely complete for a data-retrieval tool, covering data freshness, fallback mechanisms, and all filtering constraints in addition to output expectations.

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?

Given 0% schema coverage, the description is essential, providing real-world examples (e.g., 'H100', 'runpod') and rules ('all terms must match') that the schema fails to convey.

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 resource and unit ('GPU rental prices per GPU-hour') and differentiates itself by focusing on cost and providers, contrasting with sibling tools like `list_models`.

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?

The description guides usage by explaining the data origin (CSV, ENV var) and filtering logic ('cheapest first'), setting clear expectations for the agent.

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

Deploy Server