dablock.ai

Full measurement time series

get_history

Read-onlyIdempotent

Every DABLOCK release ever published, as a series per brand: share of answer at each weekly measurement with the date and panel version it was taken under.

Use this for any question about change — is a brand rising, when did it enter the index, how volatile is the category.

Two limits decide whether an answer is honest. Figures are comparable only WITHIN a panel version: the panel is frozen between releases and a version change alters the denominator, so a difference across that boundary is not a trend. And small moves sit inside language-model noise: since panel v3 (2026-08-10) each prompt runs three times per engine per release and the figure is the share of runs; earlier releases ran each prompt once, so one mention on one engine was a whole scale step there. Either way a one-step movement should not be reported as a gain or a loss. Call get_methodology for the exact step size. For the current release alone use get_visibility_index.

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
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`series`	No	Per brand slug, the share of answer at each release.
`measurements`	No

TDQS

A4.9/5.0

Behavior5/5

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

The description discloses important behavioral traits beyond the annotations: figures are comparable only within a panel version, there is language-model noise for small moves, and the data is re-measured weekly so the same call returns the same figures until the next release. It also notes licensing and no-auth requirements, enriching the readOnly/idempotent 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?

The description is front-loaded with the core purpose, followed by usage guidance, then detailed limitations and licensing. Every sentence is informative and contributes to the agent's understanding 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?

The description is fully complete for a parameterless historical data tool. It covers purpose, usage context, interpretation caveats (panel version boundaries and noise), alternatives, update frequency, and licensing, making it self-sufficient for an agent to invoke 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?

The tool has zero parameters, so the schema already covers everything. The description does not need to explain parameters; the baseline for 0 params is 4. The description adds no parameter info but that is entirely appropriate.

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 returns every DABLOCK release ever published as a per-brand time series with share of answer, date, and panel version. It also distinguishes itself from siblings by directing users to get_visibility_index for the current release and get_methodology for exact step sizes.

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 explicitly says 'Use this for any question about change' and provides exclusions: 'For the current release alone use get_visibility_index' and 'Call get_methodology for the exact step size.' This gives strong when-to-use and when-not-to-use guidance.

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 distinct purpose: one for a single brand's current standing, one for time series, one for methodology, one for the full index, and one for brand/slug lookup. The descriptions explicitly cross-reference when to use each, eliminating ambiguity.

Naming Consistency5/5

All tools follow a consistent lowercase snake_case verb_noun pattern, with four 'get_*' and one 'list_*' tool. The naming is uniform and predictable, making it easy to infer what each tool does.

Tool Count5/5

Five tools is an ideal size for this narrow, read-only domain—not too sparse, not bloated. Each tool covers a necessary aspect of accessing the DABLOCK dataset, and there is no redundancy.

Completeness5/5

The tool set provides complete coverage for the domain: retrieving the full snapshot, individual brand data, historical trends, methodology/rules, and a lookup table for slugs. No obvious missing operations exist for querying this dataset.

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