Skip to main content
Glama
Auto Ontology MCP

Auto Ontology MCP

by gogoymh

Check Answerable

check_answerable
Read-only

Checks whether the ontology covers all entities in a question before querying, returning a coverage verdict and best matches per entity to avoid substituting similar measures.

Instructions

Cheap pre-flight: does the ontology cover every entity of the question? Returns coverage, a verdict (answerable, uncertain, partially_answerable, not_answerable) and the best matches per entity. When something material is not covered, tell the user instead of substituting a similar measure.

Input Schema

NameRequiredDescriptionDefault
entitiesNo1-5 entity phrases you extracted from the question, in the ontology's language. Extract 1-5 entity phrases from the question (ported from Auto Ontology's entity coverage prompt): - Include subject nouns and domain terms (invoice, customer, shipment) and qualified phrases (order shipment, employee hire). - Keep an aggregation word attached when it names a metric (total revenue, average order value), but not when it is only how the question is asked ("how many shipments" → shipment). - Keep a multi-word filter item together (waterproof hiking tent for family camping). - Keep names that identify something: product names, brands, categories, places. - Resolve glossary abbreviations to their definition (MRR → monthly recurring revenue). - Exclude bare verbs, numbers, calendar literals, standalone status adjectives and generic words (id, name, type, code, value).
questionYesThe user's question.
datasourceNoDatasource name. Optional when only one datasource is registered.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare this is a safe read (readOnlyHint=true, destructiveHint=false, openWorldHint=false), so the bar is lower, yet the description still adds real behavior: it is cheap, it enumerates the four possible verdicts, and it returns per-entity best matches despite no match being a failure signal. The instruction not to substitute a similar measure when material coverage is missing is important behavioral guidance an agent would not otherwise know.

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?

Three sentences, front-loaded with the core framing ("Cheap pre-flight") and the return shape before the operational caveat. Nothing is padded, though the verdict enumeration could arguably be left to the output schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return values need not be re-explained, and annotations cover the safety profile; the description supplies the conceptual model and the failure-mode guidance. The one gap is that its relationship to the similarly named check_readiness sibling is left for the agent to guess.

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%, including a detailed extraction recipe for `entities` and the optional-datasource note, so the schema carries the parameter burden. The description adds no parameter-level detail beyond implying that the question's entities are what gets checked. Baseline 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 gives a specific verb and resource: it checks whether the ontology covers every entity in a question, and names the outputs (coverage, verdict, best matches). An agent can tell this is a coverage pre-flight rather than a data query. It stops short of explicitly contrasting itself with the sibling check_readiness, which reads like an adjacent pre-flight tool.

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

Usage Guidelines3/5

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

"Cheap pre-flight" implies it should run before heavier operations, and the last sentence tells the agent what to do when coverage fails. However, it never states when to use this versus check_readiness or get_query_context, and gives no explicit exclusions. Usage is inferable rather than specified.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.