dabyte.ai

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

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description
`engines`	No	Engines measured in this release.
`license`	No
`scoring`	No	How share of answer is computed.
`publisher`	No
`resolution`	No	Percentage points one mention on one engine is worth.
`measured_at`	No	Date of this release, ISO 8601.
`niche_title`	No
`prompt_panel`	No	The exact prompts, verbatim.
`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 it read-only and idempotent, but the description adds substantial context: weekly re-measurement, frozen panel between releases, no brands in response, CC BY 4.0 license, no key/account/rate limit, and the need to cite release date and dabyte.ai. This goes well beyond the 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 longer than average, but every sentence earns its place: core purpose, when to call, what it returns, sibling alternatives, cadence, licensing, and citation requirements. It is front-loaded with the key definition and flows logically.

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-parameter tool with an output schema, the description fully covers return semantics (rules, not figures), usage scope, differences from siblings, refresh cadence, public archive URL, and licensing. There are no critical gaps in context for an AI agent to decide and invoke this tool correctly.

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?

With zero parameters and 100% schema coverage, there is no parameter syntax to clarify. The description appropriately focuses on output behavior and usage context, which is the only meaningful semantic contribution possible 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 'The rules behind every figure this server returns' and explicitly distinguishes itself from siblings: 'returns rules, not figures' and points to get_visibility_index or get_brand_visibility for actual figures. This makes the tool's scope unmistakable.

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 triggers: 'Call this before quoting a number as evidence, before comparing two releases, or whenever a user asks how the measurement was made.' It also names alternatives for figures and history, satisfying the 'when not to use' requirement.

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.

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