visibility-index

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

No annotations are provided, so the description carries the full burden. It directly discloses the output dimensions (share per engine, rank, quadrant, panel prompt count) and hints at input validation by pointing to list_tracked_brands. It doesn't mention side effects, but this is a read-style get tool, and the output description is the core behavioral disclosure.

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?

Two short sentences, zero fluff. The first sentence names the resource and all key outputs; the second gives actionable parameter guidance.

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 single-parameter tool with no output schema, this description is remarkably complete. It states what it provides, what the input should be, and where to obtain valid inputs. No major gaps.

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 has 100% coverage with a clear description and examples. The description adds value by directing users to list_tracked_brands for valid slugs, which is not in the schema. This is a useful cross-reference that enhances parameter understanding.

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 'One brand's AI visibility' and enumerates the specific metrics returned (share of answer per engine, rank, quadrant, panel prompt count). This clearly distinguishes it from sibling tools like get_visibility_index or list_tracked_brands.

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 positions the tool as brand-specific and includes a cross-reference to list_tracked_brands for valid slugs. While it doesn't explicitly exclude alternatives, the 'One brand's AI visibility' framing clarifies its scope relative to siblings. This qualifies as clear context without explicit exclusions.

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?

With no annotations, the description carries the burden of behavioral disclosure. It discloses the comparability limitation within one panel version and the publication frequency (weekly), which are valuable behavioral traits. However, it does not describe the return format, ordering, or any potential side effects, though as a 'get' tool this is likely low-risk.

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-structured sentence with a colon and parenthetical caveat. It is front-loaded with the primary purpose and contains no extraneous words.

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 tool with no parameters and no output schema, the description adequately explains the expected data (share of answer per brand, weekly) and the comparability constraint. It does not specify the exact data structure or ordering, but given the simplicity, it is largely complete. Additional detail about response format could push it to a 5.

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 is empty. As per baseline for no-param tools, the description does not need to add parameter semantics. The description's mention of 'full measurement history' confirms there are no filtering inputs.

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 identifies the tool as providing full measurement history of share-of-answer per brand at weekly intervals, which distinguishes it from siblings like get_brand_visibility (current visibility) and get_visibility_index (index). While it lacks an explicit verb, the resource is specific and the scope is well-defined.

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 implies usage when historical, week-over-week share-of-answer data is needed, but it does not explicitly reference alternatives or state when not to use this tool. Sibling names are provided but not mentioned in the description, so no explicit guidance is given on choosing this tool over others.

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?

With no annotations, the description carries the full burden of behavioral disclosure. It does not state whether the operation is read-only, requires authentication, or returns static content. The description focuses entirely on content coverage rather than behavior, which is a significant gap.

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-structured sentence that front-loads the core purpose ('How the index is measured') and then lists the covered topics. Every phrase earns its place; no wasted words.

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 simplicity (no parameters, no output schema), the description covers the essential information about what the methodology includes. It is reasonably complete, though it could benefit from a note about the return format or typical usage scenarios.

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 the baseline is 4. The description appropriately avoids describing parameters that do not exist, and the schema already confirms the empty parameter set.

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's purpose: it explains how the index is measured, listing specific content areas (prompt panel, engines, scoring rules, etc.). This distinguishes it from siblings like get_visibility_index or get_brand_visibility, which focus on data rather than methodology.

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?

No guidance is provided on when to use this tool versus alternatives. The description only enumerates content topics and does not mention context, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

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?

With no annotations, the description carries the full burden. It discloses the cadence ('Weekly measurement') and the content/scope, which are useful behavioral traits. However, it does not explicitly state that this is a read-only operation or mention any output structure or limitations, though the name and content strongly imply a data retrieval.

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 sentence that packs a substantial amount of information: scope, data fields, and cadence. While slightly dense with the list of fields, it is efficient and front-loaded with the tool's purpose, making it easy to scan.

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 parameterless tool without an output schema, the description provides a good overview of what is returned (every brand, rank, shares, etc.) and the measurement frequency. It lacks explicit mention of whether it returns current-only or historical data, but the context of 'weekly measurement' suggests a snapshot, and sibling tools likely cover history.

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 description need not add parameter details. The empty schema is fully covered by the absence of properties, and the description adds no parameter-specific meaning, which is appropriate for this case.

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 that the tool provides the full DABYTE AI Visibility Index for SaaS & AI tools, enumerating the exact data returned (rank, share of answer per engine, commercial intent, quadrant). The phrase 'every tracked brand' differentiates it from sibling tools like get_brand_visibility, which likely targets a single brand.

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?

No explicit guidance is given on when to use this tool versus siblings such as get_brand_visibility, get_history, or list_tracked_brands. The scope ('every tracked brand') implies it is for the full index, but the description does not state exclusions or alternatives, leaving the choice to the agent.

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?

With no annotations provided, the description carries the full burden of behavioral transparency. It discloses that the tool returns all tracked brands with their slugs, indicating a read-only listing operation. However, it does not mention potential limitations, authentication requirements, or output format details beyond the slugs.

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, concise sentence that front-loads the key information: it lists all tracked brands in the DABYTE index and includes their slugs. There is no extraneous content or structural complexity, making it easy for an agent to parse.

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-parameter list tool, the description is complete. It specifies the exact scope ('All brands tracked') and includes the critical attribute (slugs). The sibling tools cover other purposes (visibility, history, methodology, index), so this description sufficiently differentiates the tool's purpose without needing additional 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, and the schema is an empty object with 100% coverage. Since there are no parameters to explain, the description does not need to compensate. The mention of 'slugs' is an output attribute, not a parameter, so the baseline for zero parameters applies.

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's function: returning all brands tracked in the DABYTE index with their slugs. This directly matches the tool name 'list_tracked_brands' and distinguishes it from sibling tools that focus on visibility, history, methodology, or the visibility index.

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 implies usage when needing the complete list of tracked brands, but it does not explicitly state when to use this tool versus alternatives or when not to use it. The phrase 'All brands' suggests it is for retrieving the full set, but no exclusions or comparisons to sibling tools are provided.

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