DABLOCK AI Visibility Index

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.

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.

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.

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.

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.

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.

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds crucial non-obvious behavior: figures are comparable only within a panel version, one-step movements are likely noise (with historical context on prompt runs), data is re-measured weekly so results remain stable until the next release, and licensing/attribution is disclosed. This far exceeds the annotation-provided safety profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a one-sentence summary of the tool's output, followed by when-to-use, caveats, and licensing. Although it is a multi-paragraph text, every sentence adds unique value—no filler, repetition, or ambiguity. The structure helps the agent quickly grasp the core function and then dive into interpretation constraints.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no parameters and an output schema exists, so the description's role is to provide interpretation context—and it does that thoroughly. It covers panel version comparability, noise thresholds, step-size reference, current-release alternative, weekly re-measurement, and attribution requirements. Nothing critical is missing for correct use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero properties, so no parameter descriptions are needed. The description confirms the tool returns the full series without filtering ('Every DABLOCK release ever published, as a series per brand'), which aligns with the empty schema. Baseline for zero params is 4, and no additional meaning is missing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a precise definition: '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.' It clearly identifies the tool as a historical time-series retrieval per brand and distinguishes it from siblings by directing to get_visibility_index for the current release and get_methodology for step size.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage guidance is provided: 'Use this for any question about change — is a brand rising, when did it enter the index, how volatile is the category.' It also names alternatives: 'For the current release alone use get_visibility_index' and 'Call get_methodology for the exact step size.' This makes the decision boundary crisp.

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

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

Beyond the readOnly/idempotent hints, the description discloses key behavioral traits: weekly re-measurement with stable figures between releases, that it returns no brand names, the frozen public panel, and no auth/rate limits. These add significant context not available from annotations alone, with no contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured with clear paragraphs: purpose, usage, output type, and data licensing/update cadence. It is somewhat longer than strictly necessary, but every sentence contributes meaningful information. The front-loaded purpose and usage guidance improve navigability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is highly complete given the tool's complexity and empty schema. It covers what the tool does, when to call it, how to interpret the results, the update frequency, licensing, and attribution requirements. The presence of an output schema reduces the need to detail return values, and this description fully addresses the operational context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so there are no parameter semantics to clarify. The description compensates by explaining what the response contains (rules, not figures), which aligns with the schema's empty properties. A baseline of 4 is appropriate for a no-parameter tool with thorough output description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's function: 'The rules behind every figure this server returns,' and differentiates it from sibling tools by clarifying it 'returns rules, not figures' while naming get_visibility_index, get_brand_visibility, and get_history for figures and series. This is a specific verb+resource with clear scope and sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use instructions: '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 also names alternative tools for figures and series, providing clear usage boundaries.

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

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds critical context: the data is re-measured weekly so the same call returns the same figures until the next release, it is free with no key/account/rate limit, and CC BY 4.0 licensing. It also notes the response size (~8 KB for 24 brands). 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is about 150 words but every sentence earns its place: it defines the output, gives usage guidance, lists exclusions with alternative tools, states data freshness, and notes licensing/attribution. It is front-loaded with the core purpose and contains no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has zero parameters and an output schema, the description covers all necessary context: what it does, when to use it, alternatives, data freshness, licensing, and the fact that it is not a website audit. It also mentions the sibling index at dabyte.ai for the other niche. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has no parameters and the schema is empty, so per the rubric the baseline is 4. The description does not need to explain parameters, and it appropriately confirms the tool requires no input. It additionally mentions the dataset scope (24 brands, 8 KB), which gives context about what the call returns without needing parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'The whole current release in one call' and enumerates exactly what is included: every tracked brand in crypto/Web3 with rank, share of answer, commercial intent, and quadrant. This is a specific verb+resource+scope, and it explicitly contrasts with get_brand_visibility and get_history, which distinguishes it from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('when the question is about the field — who leads, who is absent, how the category looks') and when not to, naming exact alternatives: 'get_brand_visibility is the direct answer' for a single brand, 'get_history holds the series' for trends, and clarifies it is not a website audit. This is complete guidance.

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

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

Even though annotations already declare readOnlyHint, idempotentHint, and destructiveHint, the description adds substantial context: it returns only lookup data, absence means 'not measured' not zero, covers only crypto/Web3, updates weekly, and has no authentication or rate limits. This goes well beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a bit long but every sentence contributes value: purpose, usage, contrast with sibling, caveats, data licensing. It is front-loaded with the core purpose and then expands into use cases and policies. Slightly more compact would be ideal, but it is well-organized and not redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no parameters and an output schema present, the description covers all necessary operational context: what data is returned, how to use it, what it doesn't do, its coverage scope, update frequency, and data usage terms. It is fully complete for an agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the baseline is 4. The description clarifies that the output consists of names and slugs only, which adds meaning beyond the title. Since there are no parameters, no further parameter-level detail is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists names and slugs of every brand in the DABLOCK index, explicitly framing it as a lookup table. It distinguishes itself from sibling tools by naming get_brand_visibility and get_visibility_index and explaining how it differs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases: converting a brand name to a slug for get_brand_visibility, and checking if a brand is tracked. It also gives a clear 'Do NOT use' condition with a recommended alternative (get_visibility_index), making when-to-use and when-not-to-use unambiguous.

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