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SAS MCP Server

SAS MCP Server

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by sassoftware
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Python
Remote

Get Score Execution

get_score_execution
Read-onlyIdempotent

Report score execution state, output table, and failure reason by execution ID; optionally return scored rows when complete.

Instructions

Report a score execution: its state, its output table, and why it failed if it did.

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": {
      "execution_id": "<execution_id>"
    },
    "name": "get_score_execution"
  }
}

Input Schema

NameRequiredDescriptionDefault
rowsNoWhen it has completed, also return this many scored rows (default 0, at most 100).
execution_idYesFrom ``score_table`` (or ``test_score_model`` when it ran out of time).

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.19.2

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint and closed-world behavior, so the safety profile is covered. The description adds modest value by disclosing that failure causes are returned, but says nothing about whether a run in progress is reported as partial, whether the call blocks, or what happens with an unknown/expired execution_id.

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?

A single tight sentence with the resource and return contents front-loaded and no filler. It is efficient, though perhaps slightly too lean for a tool whose main usage subtlety (polling versus reading a finished run) is left out.

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?

An output schema exists, so return shape needn't be explained, and annotations carry the safety profile. For a two-parameter read tool the description supplies the essential scope — execution status, output table, and failure diagnosis — with nothing an agent needs to call it correctly missing.

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 are documented there, including the 0-100 bound on rows and the provenance of execution_id. The description adds no meaning beyond the schema, so the baseline of 3 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?

Specific verb-plus-resource phrasing ("Report a score execution") plus the three things it returns — state, output table, failure reason — tells an agent exactly what the call yields. It does not name or differentiate against the obvious sibling (score_table), which triggers the run rather than reporting on it, so it stops 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 gives no when-to-use or when-not guidance and names no alternative tool. The only hint about the appropriate situation — that execution_id comes from score_table or test_score_model — lives in the schema, not the description, so an agent gets no explicit routing signal from the prose.

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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