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SAS MCP Server

SAS MCP Server

Official
by sassoftware
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get Castable Data

get_castable_data
Read-onlyIdempotent

Fetch rows from a CAS table with column names by providing the server, caslib, and table name, with optional limit and offset for controlled retrieval.

Instructions

Fetch rows from a CAS table with column names.

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": {
      "server_id": "<server_id>",
      "caslib_name": "<caslib_name>",
      "table_name": "<table_name>"
    },
    "name": "get_castable_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum rows to return (default 100).
startNoRow offset (default 0).
server_idYesCAS server name or ID.
table_nameYesName of the table.
caslib_nameYesName of the caslib.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
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 little behavioral context beyond 'Fetch rows,' and does not discuss pagination behavior, limits, or output structure, but it does not contradict 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 description is a single concise sentence with no filler or repetition. It front-loads the core action clearly, though it could usefully mention pagination or the distinguishing data-returning nature relative to sibling tools.

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?

The tool has an output schema, comprehensive parameter schema, and supportive annotations, so many operational details are already structured. However, given the large sibling list with similar get_castable and query tools, the description is too sparse to fully disambiguate tool selection.

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 every parameter is already documented with type, requirement, and meaning. The description adds no additional parameter-level detail beyond what the schema provides, so the baseline score of 3 is appropriate.

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 states a specific verb and resource: 'Fetch rows from a CAS table with column names.' This clearly indicates the tool retrieves data rather than metadata. It does not explicitly differentiate itself from close siblings like get_castable_columns or query_data, so it falls short of a 5.

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 offers no guidance on when to choose this tool over alternatives such as get_castable_columns, get_castable_info, list_castables, or query_data. It does not mention exclusions, prerequisites, or preferred contexts, leaving the selection decision to inference.

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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