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SAS MCP Server

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by sassoftware

Search Glossary Terms

search_glossary_terms
Read-onlyIdempotent

Find business glossary terms by free-text search matching names and definitions, with support for wildcards and field filters. Each result includes term IDs and asset count to identify terms in use.

Instructions

Free-text search of the business glossary — the way in when you know a word, not an id.

Runs against the Information Catalog's terms index, so it is ranked and matches definitions as well as names, unlike list_glossary_terms' exact structural filters. Supports the catalog grammar: wildcards (rev*), field constraints (Name:revenue, Status:Published) and + to require a word.

Each hit carries both identifiers — term_id for every other glossary tool, catalog_entity_id for catalog relationships — plus assigned_asset_count, so you can tell whether a term is actually in use before spending a call on list_term_assets. A term with a count of 0 exists in the dictionary and is attached to no data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum hits to return (default 20).
queryYesSearch text. ``*`` matches every term.
startNoOffset of the first hit (default 0).
include_attributesNoAlso return each hit's custom attributes, named (default false). The search index does not carry them, so this costs one extra batched call per 40 hits; leave it off when the names and definitions are all you need.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.14.0

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly and idempotent annotations, the description reveals search semantics: ranked matching against the terms index covering definitions as well as names, supported catalog grammar, and the dual identifier plus asset-count behavior of each hit. It also discloses the extra batched call cost of include_attributes, which annotations cannot convey.

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 organized into three dense paragraphs: a clear opening hook, grammar details in the middle, and return-value value at the end. Markdown formatting and front-loaded use case make it easy for an agent to skim and extract the key decision points.

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?

With a fully described schema and an output schema present, the description covers the remaining critical context: ranking, matching scope, catalog grammar, identifier semantics, and the cost tradeoff of include_attributes. An agent has everything it needs to decide when to call this tool and how to set parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, and the description adds real value by explaining query grammar (wildcards, field constraints, required-word operator) and the performance cost of include_attributes. It does not further enrich limit or start, but those are already self-explanatory in the schema.

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 opens with a specific verb-resource pair ('free-text search of the business glossary') and a precise use case ('when you know a word, not an id'). It also distinguishes itself from list_glossary_terms by contrasting ranked free-text search against exact structural filters.

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 frames itself as the correct entry point for word-based lookup and names list_glossary_terms as the alternative for exact structural filtering. It also gives actionable decision guidance: use assigned_asset_count to decide whether to spend a call on list_term_assets, and leave include_attributes off when names and definitions are sufficient.

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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