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quipu_load_ontology

Destructive

Parse OWL Turtle ontologies and materialize entailments to enforce class hierarchy, disjoint-class constraints, and property characteristics; also list or remove loaded ontologies.

Instructions

Manage OWL ontologies: load (parse + materialize entailments), list, or remove. Loaded ontologies enforce class hierarchy, disjoint-class constraints, and property characteristics (inverse, symmetric, functional).

Input Schema

NameRequiredDescriptionDefault
nameNoOntology name (required for load/remove)
actionNoAction to perform (default: list)
turtleNoOWL ontology in Turtle format (required for load)
timestampNoISO-8601 timestamp

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.17.0

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already flag destructiveHint=true and openWorldHint=true, so the safety profile is partly covered. The description adds genuine behavioral value by saying load materializes entailments and that loaded ontologies enforce constraints during later reasoning, but it never says that remove is the destructive branch or whether removal also discards materialized facts — the single most important consequence for an agent choosing an action.

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?

Two sentences, front-loaded with the action taxonomy and no filler. The second sentence is dense but earns its place by explaining the downstream effect of loading; a per-action risk note would have been a better use of the space than the constraint enumeration.

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?

For a four-parameter multi-action tool with full schema coverage and no output schema, the definition covers what each action does and what loading achieves. The remaining gap is return shape for list and the irreversibility of remove, which matters given destructiveHint=true.

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 every parameter is documented inline, including which are required per action (name for load/remove, turtle for load) and the default of action=list. The description adds no format or constraint detail beyond that, so the baseline 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?

Names the resource (OWL ontologies) and enumerates the three concrete actions (load, list, remove), with a parenthetical clarifying that load means parse plus entailment materialization. An agent can distinguish this from generic query tools, though it never contrasts itself with nearby siblings like quipu_shapes, quipu_validate, or quipu_namespaces.

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?

The action list implies when each mode applies, and 'loaded ontologies enforce class hierarchy, disjoint-class constraints, and property characteristics' hints at why an agent would load one. But there is no explicit when-not guidance and no routing to alternatives such as quipu_shapes for shape validation or quipu_reason for inference.

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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