dabyte.ai

DABYTE AI Visibility Index — full table

get_visibility_index

Read-onlyIdempotent

The whole current release in one call: every tracked brand in SaaS & AI tools with its rank, share of answer overall and per engine, commercial intent and quadrant. Share of answer is the percentage of a fixed panel of category buyer prompts in which an engine names the brand.

Use this when the question is about the field — who leads, who is absent, how the category looks. It is one response of roughly 8 KB for 20 brands, so prefer it over calling get_brand_visibility repeatedly.

Do NOT use it for one named brand (get_brand_visibility is the direct answer), for movement over time (get_history holds the series; a single release cannot show a trend), or to audit a website's own AI visibility — this is a measured dataset about third-party brands, not a site audit. 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

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description
`engines`	No	Engines measured in this release.
`entries`	Yes
`measured_at`	Yes	Date of this release, ISO 8601.
`niche_title`	No
`panel_version`	No	Prompt 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 read-only, idempotent, and non-destructive; the description adds valuable context: weekly re-measurement, stable results until the next release, ~8 KB response size, no auth/rate limits, CC BY 4.0 licensing, and citation requirements. This goes well beyond the structured 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?

Although lengthy, every sentence earns its place: purpose first, followed by usage, exclusions, cadence, and licensing. The front-loaded structure makes the core function immediately clear, and no information is redundant.

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?

Given the tool has no parameters and an output schema exists, the description still covers field-level use cases, sibling routing, output size, update frequency, data license, and citation obligations. It is fully complete for safe and correct invocation.

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?

There are zero parameters, so the empty schema needs no elaboration—baseline is 4. The description still adds useful dataset semantics by defining 'share of answer' and clarifying the scope of tracked brands, which helps the agent understand what the tool returns.

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 opening sentence states the tool delivers the whole current release with every tracked brand, rank, share of answer, commercial intent, and quadrant. It clearly distinguishes itself from siblings by explicitly naming get_brand_visibility, get_history, and the external index for other niches.

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?

The description provides explicit 'Use this when' conditions (field-level questions) and a 'Do NOT use it' section naming exact alternative tools and non-audit scope. This is ideal guidance for an agent choosing among siblings.

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

A4.9/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: single-brand lookup, whole-index snapshot, historical series, methodology, and brand list. Descriptions explicitly cross-reference when to use each, leaving no ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern (get_ for retrievals, list_ for enumeration), with clear object nouns. No mixed conventions or vague verbs.

Tool Count5/5

Five tools is well within the ideal range and each one earns its place in the domain. The set is neither bloated nor sparse.

Completeness5/5

The server covers the full workflow: discover tracked brands, retrieve current single-brand or index figures, access historical series, and understand methodology. No obvious dead ends or missing operations for the stated purpose.

Resources

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