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Python

Remote

Search Glossary Terms

search_glossary_terms

Read-onlyIdempotent

Find business glossary terms by keyword to see definitions and usage counts. Use wildcards and field filters to locate terms before fetching their assets.

Instructions

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

Runs against the SAS Data Governance 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

Name	Required	Description
`limit`	No	Maximum hits to return (default 20).
`query`	Yes	Search text. ``*`` matches every term.
`start`	No	Offset of the first hit (default 0).
`include_attributes`	No	Also 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

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.14.0Sep 8, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare read-only/idempotent, so the bar is met; the description goes further by disclosing that results are ranked, that hits carry both term_id and catalog_entity_id, the meaning of assigned_asset_count=0, and the real cost of include_attributes (one extra batched call per 40 hits). That is genuine behavioral context beyond the annotations.

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?

Front-loaded with the purpose and the sibling contrast, then progressively deeper detail (grammar, hit contents, cost). Paragraph breaks do real work and no sentence is filler.

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 an output schema present the description needn't document returns, yet it still adds the discriminating details an agent needs (grammar, dual identifiers, usage count, attribute-fetch cost). Nothing required to call or interpret this tool correctly is missing.

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 description coverage is 100%, so baseline is 3; the description adds query syntax the schema omits — wildcards (rev*), field constraints (Name:revenue, Status:Published) and '+' for required words. It doesn't re-explain limit/start, which the schema already covers.

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?

States a specific verb and resource ('free-text search of the business glossary') and immediately differentiates from the sibling list_glossary_terms by contrasting ranked, definition-matching search against 'exact structural filters'. An agent can pick between them without opening either schema.

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?

Explicitly frames when to use it ('the way in when you know a word, not an id'), names the alternative (list_glossary_terms) and the condition that selects it, and adds a downstream routing hint: use assigned_asset_count before spending a call on list_term_assets. Both when-to-use and when-not-to-use are covered.

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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