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sassoftware

SAS MCP Server

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by sassoftware

List Term Assets

list_term_assets
Read-onlyIdempotent

Show every column and table a business term is assigned to, giving the authoritative list of where the term is actually used.

Instructions

List the data assets a business term is attached to — the columns that mean it.

The authoritative answer to "where is this term actually used?", read from the glossaryTermAsset relationships rather than inferred from names. Each entry names the column and the table it belongs to.

An empty result means the term is assigned to nothing, which is not the same as no matching column existing — assign_glossary_term is what creates the link. For a looser, name-based sweep, catalog_search accepts the Column.term:"<term name>" facet on the datasets index, which returns matching tables without resolving columns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum assets to return in one call (default 100, ceiling 500 — the catalog's page size).
startNoOffset of the first asset returned (default 0). ``count`` in the result is the term's **total** asset count, so to read every asset of a heavily used term, call again with ``start`` = ``next_start`` until ``truncated`` is false.
term_idNoThe glossary term UUID.
term_nameNoExact term name, if the id is not known. One of the two is required.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.14.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond that: results are read from glossaryTermAsset relationships, each entry names column and table, and an empty result has distinct semantics ('assigned to nothing') rather than meaning no matching column exists.

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 front-loaded with the core purpose, then adds relationship semantics, edge-case meaning, and a concrete alternative in a compact, well-structured form. Every sentence earns its place and no content is redundant with the schema or annotations.

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?

Given the rich annotations, fully described parameters, and presence of an output schema, the description provides what is missing: the conceptual model, the meaning of empty results, and how to select between this tool and catalog_search. Nothing an agent needs to call it correctly is left out.

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 parameter descriptions are already thorough, covering defaults, ceiling, pagination via next_start/truncated, and the requirement that one of term_id/term_name be supplied. The tool description does not add parameter-level meaning beyond what the schema provides, so the baseline of 3 applies.

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?

The description names a specific verb and resource ('List the data assets a business term is attached to — the columns that mean it') and clarifies it is the authoritative relationship-based answer, not a name inference. This clearly distinguishes it from related glossary/catalog tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to use this tool ('authoritative answer to where is this term actually used?') and when not to, directing the agent to catalog_search with a specific facet for a looser name-based sweep. It also clarifies the empty-result edge case and names assign_glossary_term as the operation that creates the link.

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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