Skip to main content
Glama

model_deploy

Deploy a Ray Serve application from a Python import path to an inference target with configurable replica count.

Instructions

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

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNo
applicationYes
import_pathYes
num_replicasNo
Behavior3/5

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

The description includes a '[WRITE][risk=medium]' tag and mentions 'create/replace', partially compensating for absent annotations. However, it lacks details on side effects, permissions, or error conditions.

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 with a purpose header and a clear parameter list. Every sentence is functional with no redundancy.

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

Completeness3/5

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

While all parameters are described, the description omits return values, prerequisites (e.g., running cluster), and distinctions from siblings like deployment_redeploy, leaving gaps for a deploy tool.

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 0% schema description coverage, the description adds meaningful explanations for each parameter (e.g., 'import_path: Python import path of the Serve app (e.g. module:app)'), beyond just the schema titles.

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 Serve application from an import path' and specifies 'create/replace', making the purpose specific and distinct from siblings like model_undeploy or scale_replicas_up.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives like deployment_redeploy or when not to use it. The description only implies usage through parameter details.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AIops-tools/Inference-AIops'

If you have feedback or need assistance with the MCP directory API, please join our Discord server