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kvrancic

prime-intellect-mcp

by kvrancic

list_availability

Check available GPU pods filtered by GPU type, count, and region to find matching instances before requesting a quote.

Instructions

List currently-available GPU pods that match the filters.

Returns the SDK's GPUAvailability rows (cloud_id, gpu_type, gpu_count, prices, disk/vcpu/memory bounds, stock_status, ...). Use this to pick a target before pod_quote, or to show the user options.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gpu_typeNoGPU type slug, e.g. 'H100_80GB'. Strongly recommended — the unfiltered response is large.
gpu_countNoRequired GPU count per pod (1, 2, 4, 8). None means any.
regionsNoOptional list of region slugs. None means any.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description adequately discloses that it returns GPUAvailability rows and lists fields. It implies a read-only operation and mentions the unfiltered response is large, but could add performance or reliability notes.

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 concise with three sentences: purpose, detail on returned data, and usage guidance. No superfluous words, well-structured.

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?

Given the tool has three optional parameters and an output schema, the description covers purpose, return type, and usage context. It could elaborate on pagination or filtering behavior, but overall complete.

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 coverage is 100% and each parameter has a description. The tool's description adds value by noting that gpu_type is strongly recommended due to large unfiltered response, which goes beyond the schema.

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 currently-available GPU pods matching filters, with a specific verb and resource. It distinguishes itself from siblings by mentioning its role before pod_quote or for showing options.

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?

It explicitly says 'Use this to pick a target before pod_quote, or to show the user options,' providing clear context. However, it does not explicitly state when not to use it or compare to alternatives.

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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