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BorealHost

by ai.borealhost

create_compute_volume

Create a persistent machine and boot its first instance.

Start here, then set the machine up however you like (install packages, drivers, models). Everything you do becomes part of the volume the first time you detach or snapshot it.

While attached you pay hourly compute; while detached you pay only for stored bytes (CA$0.03/GB/month). Stopping an instance does NOT save money on this provider — detaching is what stops the compute charge.

Requires: API key with write scope, and an active subscription.

Args: name: Label for the volume, e.g. "trainer" image_id: From list_compute_images, e.g. "UBUNTU_24_04_64BIT". Cannot change later — restores must land on the same OS. instance_type: From list_compute_types, e.g. "lsw.g6.xlarge". CAN change on every later attach. ssh_public_key: Required. Volume instances always boot via cloud-init, so this is the only way in. root_disk_size: GB. Defaults to the type's minimum (or 25).

Returns: Volume dict with state "attached". Poll get_compute_volume until instance.state is "running" (~1-3 min).

Errors: PAYMENT_REQUIRED: No active subscription LIMIT_EXCEEDED: Volume cap reached NOT_AVAILABLE: Volumes not enabled on this deployment

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
image_idYes
instance_typeYes
root_disk_sizeNo
ssh_public_keyYes

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: persistent storage, hourly compute billing only when attached, that stopping an instance does not stop charges, required permissions, return state, and error conditions. No contradictions.

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

Conciseness4/5

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

Description is well-structured with paragraphs and bullet points, front-loading purpose. Slightly verbose but every sentence adds value. Could be trimmed slightly but effective.

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

Completeness5/5

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

Given 5 parameters, no output schema, and many sibling tools, the description covers: purpose, prerequisites, parameter details, return value shape, error handling, and pricing implications. It is fully informative for an AI agent.

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

Parameters5/5

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

Schema description coverage is 0%, but the Args section adds full meaning for all 5 parameters: name label, image_id source, instance_type changeability, ssh_public_key necessity, root_disk_size default. This compensates completely.

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 creates a persistent machine and boots its first instance, distinguishing it from sibling tools like attach_compute_volume or list_compute_volumes. The verb 'Create' and resource 'persistent machine' are specific.

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 tells the agent to 'Start here' and explains when to detach to save costs, providing clear context for usage. However, it lacks explicit exclusions or direct comparison to sibling tools like attach_compute_volume.

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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TDQS

A3.6/5.0
Disambiguation3/5

Several tools have overlapping boundaries, such as add_domain_dns vs manage_dns for DNS records, create_backup vs create_snapshot vs create_b2_snapshot for saved states, and write_file vs upload_file for file writing. Most tools are distinct, but these near-duplicates require agents to read descriptions carefully to avoid misselection.

Naming Consistency3/5

The naming pattern is mostly consistent verb_noun in snake_case (e.g., list_* , create_*, delete_*), but there are noticeable deviations: 'add' vs 'create' for similar operations, 'delete' vs 'remove', and outliers like manage_dns, whoami, and scale. The overall style is readable but not perfectly uniform.

Tool Count2/5

144 tools is far beyond the 'too many' threshold and would be unwieldy for agents to navigate. While the server covers a broad hosting platform with many distinct resources, the sheer number makes it difficult for each tool to earn its place, and many are simple list/get variants that could be consolidated.

Completeness4/5

The tool surface is remarkably comprehensive, covering site lifecycle, domains/DNS, compute instances/volumes, backups/snapshots, file management, databases, WordPress administration, email, security, monitoring, billing, webhooks, and support. A notable gap is the lack of a direct list_sites tool—agents must infer site slugs from list_subscriptions—but overall the domain is well covered.

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