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sassoftware

SAS MCP Server

Official
by sassoftware

publish_ml_champion_model

Destructive

Publish the champion model from an AutoML pipeline automation project to the Model Repository by specifying the project ID and destination name.

Instructions

Publish the champion model from an AutoML pipeline automation project to the Model Repository.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesID of the ML pipeline automation project.
destination_nameYesName of the destination to publish to.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv1.2.1

TDQS

A3.5/5.0
Behavior3/5

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

Annotations include destructiveHint: true, so the description must add context about destructive behavior. The description does not mention any side effects, permissions, or irreversibility. It only states the action, which is not misleading but misses an opportunity to elaborate on what 'publish' entails beyond the annotation. This is a moderate gap given the destructive hint.

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?

One sentence, no fluff, front-loads the verb 'Publish' and the object. Every word is meaningful.

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?

Given the tool has a destructive annotation and an output schema (which likely describes the published model details), the description is minimally viable. It tells the agent what it does, but lacks details on preconditions (e.g., that the champion model exists), or potential failures. The output schema covers return values, so that's not an issue, but the description could be more helpful with context about the pipeline automation project requirement.

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?

Schema description coverage is 100% and both parameters have clear descriptions. The description adds no further semantic detail beyond 'project_id' and 'destination_name', so it does not elevate beyond the baseline. The name 'destination_name' is somewhat ambiguous, but the schema clarifies it as 'Name of the destination to publish to.' No additional clarification is offered.

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 clearly states the action ('Publish the champion model') and the resource ('from an AutoML pipeline automation project to the Model Repository'). It distinguishes from sibling tools like register_ml_champion_model and publish_decision_flow, though it doesn't explicitly differentiate between those, but the specific 'champion model' and 'Model Repository' make the purpose clear.

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

Usage Guidelines3/5

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

No explicit when-to-use or when-not-to-use guidance. The context implies it is used after an AutoML project has a champion model, likely after run_ml_project. It doesn't mention alternatives or prerequisites, but the purpose is clear enough for an agent to infer.

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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