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Control Plane

by io.cpln

Convert a manifest to Terraform

convert_to_terraform
Read-onlyIdempotent

Convert a Control Plane resource manifest (YAML or JSON) into the equivalent Terraform (HCL). The manifest is first DRY-RUN VALIDATED against the API (no resource is created) — if it fails validation you get the error instead of HCL, so the returned Terraform always corresponds to a schema-valid resource. Pass gvc when the kind is GVC-scoped (workload, identity, volumeset). Set generateImports to also return ready-to-run terraform import commands. To convert an EXISTING resource instead of a manifest, use export_terraform.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gvcNoRequired only when the manifest kind is GVC-scoped (workload, identity, volumeset).
orgYesOrganization slug (lowercase kebab-case). NEVER guess — if the user has not named one, ask. On org-not-found, stop and ask; do not retry variants.
manifestYesA single Control Plane resource manifest as YAML or JSON (must include `kind` and `name`). It is dry-run validated against the API before conversion, so an invalid manifest returns the validation error instead of HCL.
generateImportsNoAlso return the matching `terraform import` commands, one per resource with the import IDs prefilled. Run them after `terraform init` and before the first `terraform apply` so the existing resources are adopted into state instead of re-created.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesWhether the call succeeded.
dataNoThe full machine-readable result — list rows, the resource object, query results. Read THIS, not just the summary.
summaryYesOne-line summary of the result.
nextStepsNoRecommended follow-up actions for this task, in order.

TDQS

A4.9/5.0
Behavior5/5

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

The description explains the dry-run validation behavior before conversion, noting that invalid manifests return an error instead of HCL. This goes beyond the readOnlyHint annotation by describing a two-phase internal process and the return-value semantics. It also clarifies the `generateImports` behavior and prerequisites, which is valuable behavioral context not present in annotations.

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 three sentences long, tightly packed with essential information: purpose, validation behavior, conditional parameter guidance, and an explicit sibling alternative. Every sentence earns its place, with no fluff or repetition of structured fields.

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 the tool's complexity (conversion + dry-run validation + optional imports) and a rich output schema, the description is complete: it covers input requirements, validation behavior, conditional parameters, and the workflow for generated imports. The presence of an output schema means return values don't need to be described, and the description still covers the key behavioral nuances for correct invocation.

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 description coverage is 100%, so the schema already documents all parameters. The description adds meaningful context beyond the schema: it explains the dry-run validation interplay with `manifest`, the conditional need for `gvc` based on GVC-scoped kinds, and the operational purpose of `generateImports` (run after init, before apply, to adopt resources). This adds value on top of 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 states a specific verb ('Convert') and resource ('Control Plane resource manifest') to Terraform (HCL), and clearly distinguishes from the sibling export_terraform by noting 'To convert an EXISTING resource instead of a manifest, use export_terraform.' This is a clear, specific purpose that differentiates it from related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool (converting a manifest) and provides an alternative (export_terraform for existing resources). It also gives command-level guidance: pass `gvc` for GVC-scoped kinds, set `generateImports` for import commands, and indirectly implies a safe dry-run validation workflow. This is explicit when/alternative guidance.

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

A4.1/5.0
Disambiguation5/5

Every tool targets a distinct resource/action pair (e.g., get_resource vs get_resource_schema, list_deployments vs list_workload_replicas) and descriptions clearly differentiate purposes. No two tools appear to do the same thing.

Naming Consistency5/5

All tools follow a verb_noun snake_case pattern (create_gvc, update_workload, list_resources, query_metrics) with consistent verbs. The few imperative verbs (browse, build, mount) still maintain the same verb-first structure.

Tool Count1/5

With 55 tools, this server far exceeds the typical well-scoped 3-15 tool range. While each tool appears purposeful, the sheer number creates selection overhead and falls into the extreme 50+ category on the rubric.

Completeness4/5

The surface covers nearly the full Control Plane lifecycle: CRUD for GVC, workload, identity, policy, volumeset, and domain, plus observability, templates, image builds, and Terraform. Minor gaps include referenced but missing configure_workload_* tools and no secret creation/deletion (by design).

Resources