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sassoftware

SAS MCP Server

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by sassoftware

publish_decision_flow

Publishes a locked decision revision to a Micro Analytic Score destination for scoring. Polls the publish job until completion and returns the module ID for direct use in score_data.

Instructions

Publish a locked decision revision to a Micro Analytic Score (MAS) destination.

Required before score_data can execute the decision — MAS runs published modules, not decision flows directly. Requires the DS2 code generation service to be healthy for this decision's rule sets; an error mentioning rule set code generation is an environment-level issue, not a bad payload.

Publishing is asynchronous and the resulting MAS module ID is server-generated — it is NOT publish_name. This polls the publish job (properties.masModules[0].jobUri) until it reaches a terminal state and returns the real moduleId alongside the publish record, so the result is directly usable with get_mas_module_step_signature/score_data without a separate lookup via list_models_and_decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
decision_idYesThe decision flow UUID.
revision_idYesThe locked revision UUID (see ``lock_decision_flow_revision``).
poll_timeoutNoMax seconds to wait for the publish job to reach a terminal state before giving up (default 60.0).
publish_nameYesThe published name shown in Model Publish (not the MAS module ID — see above).
destination_nameNoThe configured MAS publishing destination (default "maslocal").maslocal

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses asynchronous publishing, polling behavior, server-generated module ID, and direct usability with downstream tools.

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?

Well-structured, front-loaded purpose, each sentence provides new information. Efficient use of space.

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

Completeness5/5

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

Covers prerequisites, async behavior, result handling, and relationship to other tools. Complete given 5 parameters and output 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?

Schema coverage is 100%, so baseline is 3. The description adds extra meaning: explains publish_name is not the module ID, and poll_timeout default.

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?

Clearly states the tool publishes a locked decision revision to a MAS destination, distinguishes it from sibling tools by noting it's a prerequisite for score_data and contrasts with list_models_and_decisions.

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?

Explicitly states when to use (before score_data) and provides context about code generation health. Lacks explicit 'when not to use' but strong context.

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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