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deploy_prompt

Deploy a prompt version to a label (e.g., production or staging) to manage environment deployments, with automatic version tracking for one-step rollback.

Instructions

Deploy a specific prompt version to a label (an environment such as production / staging) (Pro+ only, POST /v1/prompts/:id/deploy). If a deployment already exists, the prior version is kept as previous and rollback_prompt can revert in one step. Re-deploying the same version does not create a previous entry. Labels are per prompt name (each name has its own production version).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelYesEnvironment label (alphanumerics plus _ -, 1-32 chars. E.g. 'production' / 'staging')
promptIdYesId of the version to deploy (list_prompts.prompts[].id)
Behavior3/5

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

With no annotations, the description must cover behavioral traits. It discloses key behaviors (deployment overwrite logic, per-label namespacing) and mentions the 'Pro+ only' restriction. However, it does not describe the return value, error conditions, authentication requirements, or rate limits, leaving some transparency gaps.

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 extremely concise—two sentences that each add significant value. The first sentence states the purpose and constraints, the second explains critical behavioral details. No superfluous words or redundant information.

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?

Given two parameters, no output schema, and moderate complexity, the description covers deployment behavior and references rollback_prompt. However, it lacks information about the response format (e.g., what is returned upon success/failure) and does not explicitly state that prompt IDs come from list_prompts (though schema says so). It is nearly complete for an action tool.

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?

The input schema already provides 100% coverage with descriptions for both parameters ('label' with pattern and example, 'promptId' with type and source). The description adds minimal extra meaning beyond restating the deployment context and referencing 'rollback_prompt', so a baseline of 3 is appropriate.

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 action ('Deploy a specific prompt version to a label'), identifies the resource ('prompt version'), and provides context ('environment such as production/staging'). It also mentions the HTTP endpoint and distinguishes from sibling 'rollback_prompt'.

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 explains the behavior when a deployment already exists (prior version kept) and when re-deploying the same version (no new previous). It explicitly names 'rollback_prompt' as an alternative for reverting, but does not provide guidance on when not to use the tool or contrast with other siblings like 'get_deployed_prompt'.

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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