DABLOCK AI Visibility Index

Look up one brand

get_brand_visibility

Read-onlyIdempotent

One brand's standing in the current DABLOCK release: share of answer per engine, rank, quadrant, how many panel prompts name it, and which ones.

Use this when a specific brand is named. Takes a slug, not a display name — call list_tracked_brands first if you are unsure, or read the slug from get_visibility_index.

An unknown slug is not a failure to hide: the error names every valid slug, so a second attempt can succeed. A brand absent from the index has not been measured at all, which is different from a measured zero. Only crypto/Web3 brands are tracked. For the field as a whole use get_visibility_index; for this brand over time, get_history.

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 dablock.ai when quoting a number.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	Yes	Brand slug, lowercase with hyphens — 'slack', 'coinbase', 'monday-com'. Not the display name.

Output Schema

TableJSON Schema

Name	Required	Description
`rank`	Yes	Position in this release, 1 = most named.
`slug`	Yes	Identifier used by get_brand_visibility.
`brand`	Yes	Brand name as published.
`engines`	No	Engines measured in this release.
`prompts`	No	Panel prompts in which the brand is named.
`quadrant`	No	Position on visibility against commercial intent.
`is_client`	No	Whether the brand is a client of the publisher. Placement cannot be bought; this flag makes that checkable.
`per_engine`	No	Share of answer per engine, same scale.
`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.
`visibility_score`	Yes	Share of answer, percent of panel prompts naming the brand.
`commercial_intent`	No	How commercially loaded the brand's category demand is.

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower, but the description adds valuable behavioral details: unknown slug errors name all valid slugs, absence from index vs measured zero are distinct, only crypto/Web3 brands are tracked, and data updates weekly. It also clarifies licensing and rate limits, exceeding annotation coverage.

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 long but efficiently structured: purpose first, then usage conditions, edge cases, and data policy. Every sentence provides distinct, non-redundant information, and no filler is present.

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's single parameter, rich annotations, and output schema, the description fully explains return contents, error behavior, scoping, refresh cadence, and licensing. It leaves no obvious gap for an agent to misinvoke, making it contextually complete.

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?

Schema coverage for the single slug parameter is 100%, with a descriptive pattern and examples. The description supplements this by reinforcing 'not a display name' and instructing how to obtain a valid slug via list_tracked_brands or get_visibility_index, adding practical usage meaning beyond the schema.

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, multi-faceted output definition: 'share of answer per engine, rank, quadrant, how many panel prompts name it, and which ones.' It also explicitly distinguishes from siblings by directing to get_visibility_index for the field and get_history for time series.

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 states 'Use this when a specific brand is named' and gives concrete alternatives: 'call list_tracked_brands first if you are unsure' and 'For the field as a whole use get_visibility_index; for this brand over time, get_history.' It also notes the slug requirement and how to resolve it.

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

All DABLOCK AI Visibility Index tools Inspect

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TDQS

A4.9/5.0

Disambiguation5/5

Each tool targets a distinct aspect: single-brand current status, full-index current status, historical series, methodology, and brand lookup. Descriptions explicitly cross-reference and warn against incorrect usage, leaving no ambiguity between them.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case, with 'get_' for data retrieval and 'list_' for the catalog. The pattern is predictable and uniform throughout.

Tool Count5/5

Five tools is well-scoped for a focused read-only data index. Each tool serves a necessary purpose with no redundancy or bloat, fitting comfortably within the ideal range.

Completeness5/5

The surface covers the full lifecycle of a data index service: brand lookup, current snapshots (individual and overall), historical trends, and methodology. There are no obvious gaps for the stated purpose.

Look up one brand

Input Schema

Output Schema

TDQS

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TDQS

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