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model_deploy

Deploy a Serve application to a GPU inference cluster using its Python import path. Merges into existing config while preserving current apps.

Instructions

[WRITE][risk=medium] Deploy a Serve application from an import path.

The app is merged into the cluster's declarative Serve config (existing apps preserved). Replica counts come from the app's own config; set them afterward with serve scale (deployment names are known only once the app materialises).

Args: application: Serve application name to create/replace. import_path: Python import path of the Serve app (e.g. 'module:app'). target: Inference target name from config; omit for the default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNo
applicationYes
import_pathYes
Behavior4/5

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

With no annotations, the description carries the full burden of disclosure. It clearly marks the operation as WRITE with medium risk, explains the merge behavior (existing apps preserved), and notes that replica counts come from the app's config. This provides meaningful behavioral context beyond simple action identification.

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 concise and well-organized: a clear action line, a short behavioral context paragraph, and a bulleted argument list. Every sentence contributes value, with no filler or repetition.

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?

The tool has no output schema or annotations, but the description covers the key behavioral aspects (merge, preservation, replica handling) and all parameters. It lacks explicit success/failure or verification steps, but for a deploy operation, it provides sufficient guidance to invoke correctly. Minor gaps exist around prerequisites and return behavior.

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 coverage is 0%, so the description must compensate fully. It does: each parameter is explained with its role (application name to create/replace, import path format with example, target with default behavior). This exceeds the schema's minimal titles and adds practical meaning.

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 ('Deploy') and a specific resource ('Serve application from an import path'), immediately distinguishing it from sibling tools like model_undeploy or deployment_redeploy. It also clarifies the application is created or replaced, making the action unambiguous.

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 clearly indicates when to use this tool (to deploy a Serve app) and provides a follow-up action (set replica counts with serve scale), implying that scaling is not handled here. However, it does not explicitly name alternative tools or state when not to use it, so it misses some exclusion 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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