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DABYTE AI Visibility Index

by ai.dabyte

How the index is measured

get_methodology
Read-onlyIdempotent

The rules behind every figure this server returns: the exact prompt panel and its version, which engines were measured, how share of answer is scored and rounded, the resolution of the scale in percentage points, and the editorial firewall and ownership disclosure.

Call this before quoting a number as evidence, before comparing two releases, or whenever a user asks how the measurement was made or who publishes it. It is the only tool that tells you how much of a difference is meaningful, which is what stops a one-step wobble being reported as a movement.

It returns rules, not figures — no brand appears in the response. For figures use get_visibility_index or get_brand_visibility; for the series, get_history. The panel is public and frozen between releases, so every published number can be recomputed by a third party from the archive at https://dabyte.ai/archive/.

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
enginesNoEngines measured in this release.
licenseNo
scoringNoHow share of answer is computed.
publisherNo
resolutionNoPercentage points one mention on one engine is worth.
measured_atNoDate of this release, ISO 8601.
niche_titleNo
prompt_panelNoThe exact prompts, verbatim.
panel_versionNoPrompt panel version. Figures from different versions are not comparable.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark this as read-only, idempotent, and non-destructive, but the description adds materially: returns rules not figures, no brand names, panel frozen between releases, recomputable archive, weekly re-measurement, CC BY 4.0, no auth/rate limits, and citation requirement. This goes well beyond the structured hints.

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?

Well-structured and front-loaded: first paragraph states the core purpose, second gives usage triggers, third scopes output and alternatives, fourth covers operational details (cadence, licensing). Every sentence adds distinct value without redundancy.

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?

For a zero-input methodology tool with an output schema and strong annotations, this description is complete. It explains the conceptual return type, distinguishes from data tools, gives sourcing/reproducibility and attribution context, and notes the weekly refresh behavior.

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 takes zero parameters, so input schema completely covers the interface and there is nothing for the description to add beyond the fact that no input is needed. Per the zero-parameter baseline this earns a 4.

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 'The rules behind every figure this server returns' and enumerates exactly what methodology covers (prompt panel, engines, scoring, rounding, resolution, editorial firewall). It explicitly differentiates itself from sibling tools by saying 'For figures use get_visibility_index or get_brand_visibility; for the series, use get_history.'

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?

Gives concrete triggers: call before quoting a number, before comparing releases, or when the user asks how measurement was made. It even states it is the only tool that explains meaningful differences, and points to alternatives for figures/series.

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