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tonydzi

lambda-cloud-mcp

by tonydzi

cheapest_available_instance

Find the cheapest instance with current capacity by filtering GPU count, type, region, and hourly budget. Returns availability status or false.

Instructions

Find the cheapest instance type that has capacity now. Filters: min_gpus, gpu_contains (e.g. "H100"), region (e.g. "us-east-1"), max_hourly_usd. Returns {"found": false} if nothing matches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNo
min_gpusNo
gpu_containsNo
max_hourly_usdNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses the core behavioral trait 'has capacity now' and explicitly states the fallback result {'found': false} when nothing matches. This goes beyond the schema and gives useful runtime expectations.

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?

Two short sentences: the first states the core purpose, the second lists filters and the no-match return behavior. Every word earns its place and there is no filler.

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 simple query tool with four optional parameters, the description covers all filters and the failure return. Since an output schema exists, the successful return shape does not need to be spelled out. The main gap is the absence of explicit sibling differentiation, but overall the callable details are sufficient.

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?

Schema description coverage is 0%, so the description must compensate. It does: each of the four filters is listed, and gpu_contains and region get concrete examples ('H100', 'us-east-1'). The semantics of min_gpus and max_hourly_usd are reasonably inferable from their names plus the filter context.

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 a specific verb ('Find') and a specific resource ('cheapest instance type that has capacity now'), immediately distinguishing it from list-only siblings like list_instance_types or get_instance. The mention of availability/capacity makes the purpose concrete.

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?

The description implies usage context through 'has capacity now' and the filter list, but it does not explicitly say when to prefer this tool over list_instance_types or other siblings. No when-not-to-use guidance or alternatives are named.

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