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SAS MCP Server

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by sassoftware
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Python
Remote

Get Castable Columns

get_castable_columns
Read-onlyIdempotent

Retrieve column metadata (names, types, labels, formats) for a CAS table using server, caslib, and table names. Missing tables produce a structured not_found response explaining common causes.

Instructions

Get column metadata for a CAS table (names, types, labels, formats).

A missing table returns a structured not_found with the two usual causes (unloaded source table vs session-scoped table) instead of a raw HTTP error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum columns to return (default 200).
server_idYesCAS server name or ID.
table_nameYesName of the table.
caslib_nameYesName of the caslib.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.7.0
    • addedOutput schema / properties / result / anyOf
      Added value: +[
+  {
+    "items": {
+      "additionalProperties": true,
+      "type": "object"
+    },
+    "type": "array"
+  },
+  {
+    "additionalProperties": true,
+    "type": "object"
+  }
+]
    • removedOutput schema / properties / result / items
      Removed value: -{
-  "additionalProperties": true,
-  "type": "object"
-}
    • removedOutput schema / properties / result / type
      Removed value: -"array"
  2. Changed2 schema fields changedv1.2.0
    • addedOutput schema / properties / result / items / additionalProperties
      Added value: +true
    • addedOutput schema / properties / result / items / type
      Added value: +"object"
  3. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context by explaining that a missing table returns a structured not_found with the two usual causes instead of a raw HTTP error, which is genuinely useful for an agent handling failures.

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 compact and front-loaded with the core purpose. The first sentence states exactly what the tool does, and the second adds a meaningful error-behavior note. No filler or redundancy is present.

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?

Given the read-only annotations, fully documented parameters, and presence of an output schema, the description covers the essential operational behavior well, including the missing-table edge case. It could be more complete by naming alternatives or clarifying when to prefer this over the similarly named column/table tools, but nothing critical is missing for calling the tool correctly.

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%, so the schema already documents all parameters clearly. The description adds no additional parameter-level meaning beyond framing the operation as column metadata retrieval; it does not explain relationships, formats, limits, or edge cases for server_id, caslib_name, or table_name.

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 verb ('Get'), the resource ('column metadata for a CAS table'), and the specific fields returned ('names, types, labels, formats'). This is more precise than the bare title and distinguishes it from data-returning tools like get_castable_data, though it does not explicitly differentiate from sibling tools such as list_compute_columns.

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?

The description gives no explicit guidance on when to use this tool versus alternatives like list_compute_columns, list_castables, or get_castable_info. There are no prerequisites, exclusions, or conditional routing hints; the usage context is only implied by the tool's purpose.

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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