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SAS MCP Server

SAS MCP Server

Official
by sassoftware
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get Model

get_model
Read-onlyIdempotent

Describe a registered model's project, version, target, variables, files, event probability, and champion role before scoring, publishing, or monitoring; confirm required scoring inputs.

Instructions

Describe a registered model: project, version, target, variables and files.

Use it before scoring, publishing or monitoring a model, to see its input variables (what a scoring table must hold), its target and event probability variable, and whether it is the project's champion (role).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model_id": "<model_id>"
    },
    "name": "get_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_idYesThe model's ID (see ``list_registered_models``).

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.19.2

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context, namely that the response exposes input variables (what a scoring table must hold), the target and event probability variable, and champion status via role. It does not mention permissions or failure modes, so it adds moderate value beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose statement is front-loaded in one line, followed by a short usage sentence that explains why each returned field matters. There is no filler, though the parenthetical glosses add some length that could be trimmed.

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?

With an output schema present, the description need not explain return values, and it still usefully previews what the response contains. For a single-parameter read tool with full annotation coverage, this is nearly complete; only permission or error behavior is unaddressed.

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 coverage is 100% and the single model_id parameter is already documented in the schema, including a pointer to list_registered_models. The description adds no further meaning about the identifier format or lookup behavior, so the baseline applies.

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 pairs a specific verb ("Describe") with a specific resource ("a registered model") and enumerates the attributes returned: project, version, target, variables and files. That is more informative than the title, though it does not explicitly contrast itself with siblings such as list_model_versions or list_registered_models.

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?

It gives a clear usage context: call it before scoring, publishing or monitoring a model. It does not state when not to use it or name an alternative tool for adjacent needs, so it stops short of full when/when-not/alternatives 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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