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DABYTE AI Visibility Index

by ai.dabyte

List tracked brands and slugs

list_tracked_brands
Read-onlyIdempotent

The names and slugs of every brand in the DABYTE index — a lookup table, nothing else. No scores, no ranks.

Use it for two things: to turn a brand name into the slug get_brand_visibility needs, and to answer whether a brand is tracked at all.

Do NOT use it when you want figures — get_visibility_index returns the same brands with their full measurements in a single call, so calling this one first is a wasted round trip. Absence here means the brand is not measured, not that it scores zero. Covers SaaS & AI tools only; the sibling index at dablock.ai covers the other niche.

Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandsYes

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint), the description adds valuable context: data is re-measured weekly, absence means 'not measured' rather than a zero score, and the API is free with no rate limit. It also clarifies the scope (SaaS & AI tools only). No contradictions with annotations.

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 well-structured in short paragraphs, each sentence earning its place. It covers purpose, use cases, exclusions, update cadence, and licensing without redundancy, making it efficient and scannable.

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

Completeness5/5

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

With an output schema present, return value details are already covered. The description sufficiently explains the tool's role, usage boundaries, data freshness, and licensing. It is complete for an agent to decide when and how to invoke it.

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?

The tool has zero parameters and the schema coverage is 100%, so the baseline is 4. The description adds the semantic note that it returns only names/slugs and that absence is meaningful, but since there are no parameters, no further param documentation is needed.

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 clearly states the tool lists names and slugs of every brand in the DABYTE index, explicitly framing it as 'a lookup table, nothing else. No scores, no ranks.' It distinguishes itself from the sibling get_visibility_index by noting that tool returns full measurements, making the purpose unambiguous.

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

Usage Guidelines5/5

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

It provides explicit use cases: converting a brand name to a slug for get_brand_visibility and checking if a brand is tracked. It also gives a direct 'Do NOT use it when you want figures' with an alternative (get_visibility_index), clearly guiding when to avoid this tool.

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

All DABYTE AI Visibility Index toolsInspect
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TDQS

A4.9/5.0
Disambiguation5/5

Each tool has a unique, well-defined purpose: single-brand current data, full index current data, historical series, methodology rules, and brand lookup. No two tools overlap in what they return, and the descriptions explicitly cross-reference when to use each one.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case: get_ for data/methodology retrieval and list_ for the lookup table. The one list_ verb is appropriately distinct and still fits the convention.

Tool Count5/5

Five tools is well-scoped for a specialized visibility index. Each tool covers a necessary access pattern (one brand, all brands, history, methodology, brand list) without redundancy or bloat.

Completeness5/5

The surface fully covers the domain: current individual and aggregate views, historical series, measurement rules, and brand membership. No obvious gaps are present for the stated niche of the DABYTE index.

Resources