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inspect

Explore a FIBO entity's local graph neighborhood, returning its URI, labels, definitions, parent/child classes, and restrictions in compact JSON.

Instructions

Inspect one FIBO class/entity as an LLM-friendly local graph neighborhood.

Use this after discovering a compact URI such as fibo-sec-eq-eq:Share. It returns compact JSON with the queryable URI, labels, definitions, direct parent/child classes, and direct OWL restrictions. This is usually better than asking for only a bare URI because LLMs understand terminology and incident neighborhoods more reliably than isolated graph handles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
identifierYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description is the sole source of behavioral transparency. It discloses that the output is 'compact JSON' containing specific fields (queryable URI, labels, definitions, direct parent/child classes, direct OWL restrictions), and frames it as a 'local graph neighborhood'. However, it does not explain the effect of the 'limit' parameter or error/edge-case behavior.

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 three sentences: purpose, usage scenario, and rationale. Every sentence adds value, with technical details packed into the second sentence while keeping the overall text short.

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?

The tool is simple with only two parameters, and an output schema exists, so the return structure is already specified. The description adds usage context and output content expectations, but the unidentified 'limit' parameter leaves a minor gap.

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 coverage is 0%, so the description must fully document parameters. It provides an example identifier ('fibo-sec-eq-eq:Share') and implies it is a compact URI, which helps with the required 'identifier' parameter. But the optional 'limit' parameter is never mentioned, leaving its semantics undocumented.

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 'Inspect one FIBO class/entity as an LLM-friendly local graph neighborhood', clearly identifying the verb (inspect), resource (FIBO class/entity), and scope (local graph neighborhood). This differentiates it from the sibling 'sparql' tool, which is a more generic query interface.

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

Usage Guidelines4/5

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

It provides explicit usage context: 'Use this after discovering a compact URI such as fibo-sec-eq-eq:Share.' It also explains why this tool is preferable ('usually better than asking for only a bare URI'), but it does not explicitly mention when to avoid it or name alternatives, just compares to a bare URI approach.

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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