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TestGraph

by io.github.BBCBasic

Inspect standard vocabulary

vocabulary_index
Read-onlyIdempotent

List canonical subject types, aliases, flexible relationships and reusable fields. Inspect this before classifying any unknown subject type. There are no DNS storage paths or review leaf concepts. Naming disagreement is soft and must not block use. If two labels are genuinely equivalent, they may resolve to the same stable subject-type identity through an alias even when different AI clients prefer different display names. Do not require cross-model agreement on wording before using an existing type. Semantic disagreement is different: disagreement about whether two concepts mean the same thing, or about a belongs_to/other relationship, may require preservation as separate concepts or a deliberation rather than silently collapsing them. Classification vocabulary should represent what a subject fundamentally is. Before creating, selecting, relating or proposing a subject type, identify the semantic head and descriptive modifiers. Material, arrangement/grouping, state/condition, quantity, colour, size, location and purpose/use normally belong in attributes or relationships rather than subject-type names. This is not a simplistic head-noun rule: a compound may remain a distinct type when the combined concept has materially different identity, behaviour, relationships, classification meaning or realistic retrieval needs. The server independently validates structural writes, so client guidance cannot bypass this rule.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds meaningful context about alias resolution, the distinction between naming and semantic disagreement, and server-side validation. It does not mention return format or pagination, but the annotations carry much of the safety burden.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear purpose statement, but the latter half becomes a long policy discussion about semantic heads, modifiers, and server validation that goes beyond what is needed to invoke a parameterless listing tool. It is dense but overlong.

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?

Given the zero-parameter schema, read-only annotations, and no output schema, the description adequately explains what the tool exposes and how to interpret aliases and identity. It does not explicitly detail the response shape, but the opening sentence names the returned content types.

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?

This tool has zero parameters and schema coverage is 100%, so the description has no parameter semantics to add. Baseline 4 is appropriate for a parameterless tool.

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 a specific verb and resource: 'List canonical subject types, aliases, flexible relationships and reusable fields.' This clearly identifies what the tool does and distinguishes it from sibling classification and resolution tools by positioning it as the reference inspection operation.

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?

The description explicitly says to inspect this before classifying an unknown subject type and before creating, selecting, relating, or proposing a subject type. This gives clear when-to-use context, though it does not explicitly name alternative sibling tools or provide when-not conditions.

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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TDQS

A3.6/5.0
Disambiguation3/5

Most tools are separated by resource and action, but the classification cluster (affirm_subject_classification, propose_subject_reclassification, reopen_subject_classification, get_subject_classification) and the three resolve_subject* tools require close reading to distinguish. Long descriptions help, but an agent could easily pick the wrong member of those clusters.

Naming Consistency4/5

The dominant pattern is verb_noun in snake_case (create_deliberation, list_reviews_by_visibility, resolve_subject_type, set_review_visibility). Minor deviations such as bare 'fetch' and 'search' and mixed verbs like affirm vs propose vs reopen are readable and do not break the convention.

Tool Count2/5

34 tools substantially exceeds the 25+ threshold even for a server with multiple subdomains. The many classification, type, and location variants add cognitive load, and several could be consolidated or hidden behind a smaller workflow-oriented surface.

Completeness4/5

The set covers the main lifecycles: review save/fetch/assess/delete/visibility, subject resolution/enrichment/correction, classification with voting/reopening, deliberations, locations, and vocabulary management. Minor gaps remain, such as no direct full-subject getter and no update to review text, but these appear intentional in an immutable-review design.

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