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Workload catalog

list_templates
Read-only

Find ready-to-run workloads with actual VRAM and requirements. Compare GPU options for video models and deploy compute tasks on Vast.ai efficiently.

Instructions

Ready-to-run workloads with their real VRAM, architecture and download requirements. MiniMax H3 is the best open-weight video model; LTX-2.5 is much faster and cheaper per clip. Returns JSON: {ok, message, next_tool, next_args, ...tool-specific fields}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesfalse only when the tool itself failed (bad input, missing key, API error).
messageYesHuman-readable summary to relay to the user.
next_argsNoSuggested arguments for next_tool.
next_toolNoSuggested next tool, or null when nothing else is needed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already cover readOnlyHint=true and destructiveHint=false. The description adds a concrete return envelope ({ok, message, next_tool, next_args, ...}) and emphasizes that the data reflects 'real' requirements, which is useful behavioral context beyond the annotations. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The first sentence and return-format note are efficient and useful. The middle sentence about MiniMax H3 and LTX-2.5 is promotional, subjective, and unnecessary for selecting or invoking the tool, adding noise without operational value.

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?

For a zero-parameter read-only list with an output schema, the description is mostly adequate. However, it does not explain what next_tool/next_args mean or how an agent should proceed after receiving them, and it gives no usage context relative to the deployment workflow.

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?

The tool has zero parameters and schema coverage is 100%, so the description carries no parameter burden. The baseline for a zero-parameter tool is 4.

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 identifies the resource as 'Ready-to-run workloads' and specifies the key attributes returned (VRAM, architecture, download requirements), making it clear this is a catalog/list operation. It does not explicitly name a sibling, but the content is distinct from list_gpus and get_template. Some extraneous model commentary slightly weakens the focus.

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 explicit guidance about when to use this tool versus alternatives like get_template, list_gpus, or recommend_plan. The description implies browsing workloads before deployment, but never states a condition, prerequisite, or exclusion.

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