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KG Add Triple

localnest_kg_add_triple

Add facts to a knowledge graph by specifying a subject, predicate, and object. Entities are auto-created, and contradictory statements are detected.

Instructions

Add a subject-predicate-object triple to the knowledge graph. Entities are auto-created on first reference. Detects contradictions (same subject+predicate with different valid object) and warns without blocking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
subject_nameYes
predicateYes
object_nameYes
subject_idNo
object_idNo
valid_fromNo
valid_toNo
confidenceNo
source_memory_idNo
source_typeNomanual
terseNoverbose
response_formatNojson

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
metaYes
Behavior4/5

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

Annotations already indicate a write operation (readOnlyHint=false) and non-destructive nature (destructiveHint=false). The description adds valuable context: entities auto-create on first reference and contradictions are warned without blocking. This goes beyond annotations, though some details (like return structure) are missing.

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 two sentences long, front-loaded with the main action, and contains no superfluous information. Every sentence adds value.

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

Completeness3/5

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

Given the tool's complexity (12 parameters, behavioral nuances), the description is somewhat brief. It covers the core purpose and key behaviors but omits details like how to use optional parameters, expected return values (though output schema exists), and interaction with other KG tools. It is minimally adequate.

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

Parameters2/5

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

With 0% schema description coverage, the description is expected to explain parameters. It only hints at the three required ones (subject, predicate, object) but does not clarify the many optional parameters (e.g., confidence, valid_from, source). This is insufficient for effective usage.

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 clearly states the action (add a triple) and the resource (knowledge graph). It mentions auto-creation and contradiction detection. However, it does not explicitly differentiate from sibling tools like batch or entity-only tools, so it is not a 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives (e.g., batch version for multiple triples). It does not mention prerequisites or when to avoid using it.

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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