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by dev.ployz
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deploy

Destructive

Deploys everything staged. Deploying every Service removes other Services running in the Environment, unless no_prune. With skip_deploy, saves it for the next deploy instead. A deploy queues a Deployment and returns without waiting for it to finish. Every deploy waits for a human to approve; a save waits only if the next deploy removes a Service, a Volume, or a live domain. May stop to ask a human to approve.

Input Schema

NameRequiredDescriptionDefault
messageNoWhat this ships, in a few words. Shown on the Deployment.
projectNoProject name.
versionNoThe reviewed version; refused if what is staged changed since. Required with accept_volume_loss.
approvalNoThe ID of an approval a person granted. Send it when calling this tool again after it answered approval_required.
no_pruneNoKeep running Services the Environment doesn't hold.
servicesNoDeploy only these Services. Omit to deploy every Service.
environmentNoEnvironment name, e.g. staging.
request_keyNoAny id you choose. A call that repeats a key gets the first call's result, not a second deploy.
skip_deployNoSave everything staged for the next deploy instead of deploying it.
accept_volume_lossNoVolumes whose data this may delete.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / no_prune
      Added value: +{
+  "description": "Keep running Services the Environment doesn't hold.",
+  "type": "boolean"
+}
  2. First observed

TDQS

A4.2/5.0
Behavior5/5

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

Goes well beynd the annotations: it discloses that pruning removes Services unless no_prune, that Volumes may be destroyed (accept_volume_loss), that the call is asynchronous and returns before completion, and that every deploy requires human approval.

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?

Front-loaded with the core action followed by scoping caveats and async/approval behavior. Each sentence carries weight, though the final sentence slightly repeats the earlier approval point.

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 10-parameter destructive mutation with no output schema, the description covers the destructive, async, and approval traits thoroughly. It omits return shape and idempotency, which are left to the schema.

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?

With 100% schema coverage the baseline is 3; the description adds cross-parameter meaning by tying no_prune to pruning, skip_deploy to a save, and approval to the approval_required retry flow.

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 states a specific verb and resource ('Deploys everything staged') and explains the staging model, but it never names a sibling alternative (e.g., discard or deployment_start) to differentiate itself.

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?

It clearly specifies when to use skip_deploy (save instead of deploy) and no_prune (keep running Services), and explains the approval flow. It stops short of explicit when-not guidance or naming alternatives.

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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