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deepinfra

Get GPU availability

deepinfra_get_gpu_availability
Read-only

Get GPU availability for LLM deployments (which hardware can currently be provisioned). DeepInfra REST: GET /deploy/llm/gpu_availability.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNoFilter by source.
base_modelNoFilter by base model.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered for this pure read. The description adds useful context by mapping the tool to the underlying REST endpoint (GET /deploy/llm/gpu_availability) and noting that results reflect current provisionability. It does not disclose auth requirements, how dynamic/volatile the answer is, or the response shape.

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 compact clauses with zero waste: the purpose and the mechanism are front-loaded, and the endpoint reference is a single trailing clause. Nothing needs trimming.

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

Completeness3/5

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

With no output schema, the description should hint at what comes back (e.g. a list of GPU types with availability), but only says 'which hardware can currently be provisioned'. Combined with the absent usage guidance against deepinfra_get_hardware, the definition is serviceable but leaves gaps for a read tool with two undocumented-augmenting filters.

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

Parameters3/5

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

Schema description coverage is 100% (both filters documented: 'Filter by source' and 'Filter by base model'), so the schema carries the parameter burden. The description adds nothing about what 'source' means or what values base_model accepts, which is the baseline 3 case when the schema does the heavy lifting.

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?

States a specific verb and resource ('Get GPU availability for LLM deployments') and clarifies the meaning of availability with '(which hardware can currently be provisioned)'. It does not explicitly differentiate from the sibling deepinfra_get_hardware, so the agent must infer the distinction between a static hardware catalog and live provisioning availability.

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?

There is no when-to-use guidance, no prerequisites, and no alternative named. The closest sibling, deepinfra_get_hardware, is not mentioned, and the agent is not told that this is the pre-flight check before deepinfra_start_deployment. The parenthetical implies the purpose but never states the condition for calling it.

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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