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Create (and start) a deployment

create_deployment

Launch a Nosana GPU deployment from a template or custom job definition, start it on the selected GPU, and receive a deployment ID to poll for readiness.

Instructions

SPENDS CREDITS. Creates a Nosana deployment from a template or job definition on the chosen GPU and starts it. Requires confirm=true, which you must only pass after the user has seen the estimate from estimate_deployment and agreed. Returns the deployment id; then poll wait_for_deployment until the endpoint is ready or the job completes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gpuYesGPU market slug, short name or address, e.g. "nvidia-5090" or "5090" (see list_gpus).
nameNoDeployment name (default: <template>-<timestamp>).
forceNoDeploy despite blocking warnings (too little VRAM, non-Blackwell GPU, insufficient credits).
startNoStart immediately (false leaves a DRAFT; note drafts cannot be deleted until started once).
confirmNoMust be true. Confirms the user approved the estimated cost.
variantNoVariant id, e.g. "i2v-32gb". Required when the template has variants.
replicasNoParallel jobs.
scheduleNoCron expression (5 fields). SCHEDULED strategy only.
strategyNoSIMPLE runs once and stops at the timeout (predictable cost). SIMPLE-EXTEND keeps extending while credits last. SCHEDULED needs schedule. INFINITE keeps a replacement job ready.SIMPLE
templateNoTemplate id or name, e.g. "minimax-h3" (see list_templates). Omit when passing job_definition.
confidentialNoHide the job on the explorer and protect the endpoint with an auth header.
job_definitionNoA custom Nosana job definition object instead of a template.
timeout_minutesNoMinutes the GPU is reserved per job. Nosana refuses anything below 60. Big templates such as MiniMax H3 want 120 or more.
include_community_gpusNoAllow community GPU markets, not only the premium ones the dashboard shows.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  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?

Annotations only provide generic hints (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false). The description adds crucial behavioral context beyond these: 'SPENDS CREDITS,' requiring user-approved confirm=true, and returning a deployment id followed by polling wait_for_deployment. This is strong disclosure for a credit-spending action.

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?

Three sentences, each earning its place: the first front-loads the credit warning and core action; the second states the mandatory confirmation workflow; the third explains the return value and follow-up polling. No filler, no redundant restatement of schema details.

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 complex tool with 14 parameters, a nested job_definition object, and no output schema, the description covers the essential high-level workflow: estimate → confirm → create/start → poll. It does not redundantly list every parameter, leaving that to the schema. It could have mentioned edge cases like draft mode (start=false), but the schema handles that, so this is reasonably complete.

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%, so the schema fully documents all 14 parameters. The description mentions 'template or job definition' and 'chosen GPU' and 'confirm=true', but these are already elaborated in the schema with examples and conditions. It adds no substantive meaning beyond the structured parameters.

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 states a specific action and resource: 'Creates a Nosana deployment from a template or job definition on the chosen GPU and starts it.' It clearly distinguishes this from the sibling start_deployment (which starts an existing deployment) by combining create and start. It also references estimate_deployment and wait_for_deployment, further separating its role in the workflow.

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 gives explicit usage guidance: 'Requires confirm=true, which you must only pass after the user has seen the estimate from estimate_deployment and agreed.' It also maps the lifecycle after creation: 'then poll wait_for_deployment until the endpoint is ready or the job completes.' It does not explicitly state when not to use it in favor of start_deployment, but the create-and-start framing implies 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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