DABYTE AI Visibility Index

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

Beyond annotations (readOnly, idempotent, non-destructive), the description adds valuable behavioral context: weekly re-measurement ('same call returns the same figures until the next release'), error behavior for unknown slugs (lists all valid slugs), the distinction between absent and measured-zero, and licensing/auth details (CC BY 4.0, no key, no rate limit). This greatly exceeds what annotations alone 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 front-loaded with purpose, then systematically covers usage, error semantics, refresh cadence, and licensing. Every sentence adds information; there is no fluff or repetition. It is long but dense and well-organized.

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 moderate complexity, the description covers all needed context: when to use, how to get the slug, error handling, data freshness, absence semantics, licensing, and alternative tools. It is comprehensive for a single-parameter lookup tool with good annotations and an output schema.

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 schema already covers the slug parameter well (pattern, format examples, 'Not the display name'). The description reinforces this and adds practical guidance: 'call list_tracked_brands first if you are unsure' and 'read the slug from get_visibility_index.' Since schema coverage is 100%, the baseline is 3, and the extra usage context earns a 4.

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 statement of what the tool returns: 'share of answer per engine, rank, quadrant, how many panel prompts name it, and which ones.' It names the specific resource (one brand's standing in the current DABYTE release) and immediately differentiates from siblings by directing to get_visibility_index for the whole field and get_history for temporal trends.

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: 'Use this when a specific brand is named.' It also gives concrete alternatives: 'For the field as a whole use get_visibility_index; for this brand over time, get_history.' It even advises calling list_tracked_brands first if uncertain about the slug, showing clear decision 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds crucial behavioral context beyond annotations: the panel version freeze and denominator change, the three-run measurement per engine since panel v3, the weekly refresh cycle, and the lack of auth/rate limits. It also clarifies data licensing and attribution requirements.

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 dense but every sentence earns its place: it leads with the core definition, then usage guidance, then critical interpretative caveats, then update frequency and licensing. No redundant or filler content; it is efficiently structured with distinct logical segments.

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 zero parameters, rich annotations, and an output schema, the description covers everything needed: what data is returned, how to interpret it (panel version boundaries, noise thresholds), how often it updates, and access requirements. It even tells users not to report step movements as gains/losses, which is essential operational guidance.

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 schema coverage is trivially 100%. The description adds no parameter semantics (there are none to add) but provides valuable context about the output's scope and interpretation, which is the only meaningful semantic content for this no-input tool. Baseline for zero parameters is 4.

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 what the tool does: it returns every DABYTE release ever published, as a series per brand, with share of answer, date, and panel version. It explicitly contrasts with sibling tools: 'For the current release alone use get_visibility_index' and 'Call get_methodology for the exact step size,' distinguishing it from alternatives.

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 ('Use this for any question about change — is a brand rising, when did it enter the index, how volatile is the category') and provides clear exclusions: comparability is invalid across panel versions, and small moves within noise should not be reported. It also names alternative tools for specific use cases (get_methodology, get_visibility_index).

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 mark this as read-only, idempotent, and non-destructive, but the description adds materially: returns rules not figures, no brand names, panel frozen between releases, recomputable archive, weekly re-measurement, CC BY 4.0, no auth/rate limits, and citation requirement. This goes well beyond the structured hints.

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?

Well-structured and front-loaded: first paragraph states the core purpose, second gives usage triggers, third scopes output and alternatives, fourth covers operational details (cadence, licensing). Every sentence adds distinct value without redundancy.

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?

For a zero-input methodology tool with an output schema and strong annotations, this description is complete. It explains the conceptual return type, distinguishes from data tools, gives sourcing/reproducibility and attribution context, and notes the weekly refresh behavior.

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 takes zero parameters, so input schema completely covers the interface and there is nothing for the description to add beyond the fact that no input is needed. Per the zero-parameter baseline this earns a 4.

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 rules behind every figure this server returns' and enumerates exactly what methodology covers (prompt panel, engines, scoring, rounding, resolution, editorial firewall). It explicitly differentiates itself from sibling tools by saying 'For figures use get_visibility_index or get_brand_visibility; for the series, use get_history.'

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?

Gives concrete triggers: call before quoting a number, before comparing releases, or when the user asks how measurement was made. It even states it is the only tool that explains meaningful differences, and points to alternatives for figures/series.

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, openWorldHint, and destructiveHint, setting a strong baseline. The description goes beyond by adding behavioral details: 'Re-measured weekly, so the same call returns the same figures until the next release' and 'no key, no account, no rate limit — cite the release date and dabyte.ai.' This informs the agent about data freshness, caching semantics, and licensing requirements—none of which are in 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 a single well-organized paragraph with clear topic flow: what it does, when to use it, when not to use it (with alternatives), and key data characteristics. No sentence is redundant—each one adds either clarity about scope, usage boundaries, or data behavior. It is appropriately sized for a tool with several important caveats.

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 an output schema present, the description doesn't need to explain return fields, yet it still covers the essential contextual aspects: dataset scope (SaaS & AI tools only), data update cycle, licensing, citation requirements, and relationship to sibling tools. It also mentions the sibling index at dablock.ai as a comparison. This is fully complete for an agent to invoke confidently.

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 (schema coverage 100% by vacuity), so the baseline for this dimension is 4. The description doesn't need to explain parameters, but it does add useful context about the response size ('roughly 8 KB for 20 brands') and the data fields included, which helps set expectations. It earns the baseline score without needing extra parameter-related elaboration.

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, specific definition: '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.' It names the exact data entities and distinguishes from siblings like get_brand_visibility and get_history, making it unmistakable what this tool returns.

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 guidance is provided: 'Use this when the question is about the field — who leads, who is absent, how the category looks.' It also gives concrete negative directives: 'Do NOT use it for one named brand... for movement over time... or to audit a website's own AI visibility,' each paired with the correct alternative tool. This is textbook usage 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?

Beyond the annotations (readOnlyHint, idempotentHint), the description adds valuable context: data is re-measured weekly, absence means 'not measured' rather than a zero score, and the API is free with no rate limit. It also clarifies the scope (SaaS & AI tools only). No contradictions with 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 well-structured in short paragraphs, each sentence earning its place. It covers purpose, use cases, exclusions, update cadence, and licensing without redundancy, making it efficient and scannable.

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 an output schema present, return value details are already covered. The description sufficiently explains the tool's role, usage boundaries, data freshness, and licensing. It is complete for an agent to decide when and how to invoke it.

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 and the schema coverage is 100%, so the baseline is 4. The description adds the semantic note that it returns only names/slugs and that absence is meaningful, but since there are no parameters, no further param documentation 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 DABYTE index, explicitly framing it as 'a lookup table, nothing else. No scores, no ranks.' It distinguishes itself from the sibling get_visibility_index by noting that tool returns full measurements, making the purpose unambiguous.

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 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 direct 'Do NOT use it when you want figures' with an alternative (get_visibility_index), clearly guiding when to avoid this tool.

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