visibility-index

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

With no annotations, the description must convey behavior itself. It lists the output fields (share, rank, quadrant, prompt counts), which implies a read-only query. However, it does not mention side effects, error behavior for invalid slugs, or whether data is current, leaving some transparency gaps.

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 two sentences, front-loaded with the core purpose and output metrics, then a direct prerequisite reference. Every phrase contributes meaning with no filler or 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?

Given the tool has only one parameter and no output schema, the description is complete enough: it states the resource, the output dimensions, and how to obtain valid input. It could explicitly note that it is read-only or describe the response format, but for a simple lookup tool, the coverage is strong.

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 for the single slug parameter with an example. The description adds value by pointing to list_tracked_brands for valid slugs, which is helpful context beyond the schema. This goes beyond the baseline for high schema coverage, so a 4 is appropriate.

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 states the tool returns a single brand's AI visibility metrics including share of answer per engine, rank, quadrant, and panel prompt counts. It clearly identifies the resource (one brand) and differs from sibling tools like get_visibility_index by focusing on a single brand and listing concrete output dimensions.

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 the tool is for retrieving per-brand visibility metrics and explicitly directs users to list_tracked_brands for valid slugs, giving clear context on a prerequisite. It does not explicitly contrast with get_visibility_index or get_history, but the single-brand focus makes the intended use case clear.

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 must carry the disclosure burden. It adds context about content (share of answer) and the comparability constraint, which is useful. However, it does not mention output format, data volume, or any explicit read-only behavior. As a simple history retrieval tool, this is adequate but not rich.

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, information-dense sentence that is front-loaded with the primary purpose ('Full measurement history'). Every clause earns its place, with no fluff or 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 no-parameter tool without an output schema, the description is fairly complete: it specifies the metric (share of answer), the grouping (per brand), the cadence (weekly), and the comparability caveat (panel version). The lack of return format details is a minor gap, but the essence is covered.

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 elaborate further. The empty schema already provides full coverage. The description adds context about what the history includes, which is sufficient for a parameterless tool.

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: retrieving full measurement history with specific content (share of answer per brand) and temporal scope (every published weekly measurement). It distinguishes itself from siblings by focusing on time-series history rather than current visibility or 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?

The description provides no explicit guidance on when to use this tool versus alternatives like get_brand_visibility or get_visibility_index. No exclusions or alternative recommendations are offered. The 'comparable within one panel version' caveat implies a limitation but does not clarify appropriate use cases.

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 burden of behavioral disclosure. It lists the content topics (scoring rules, editorial firewall, etc.), giving a good sense of what the response will contain, but it does not mention whether the operation is read-only, any authorization needs, or the response format.

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, front-loaded sentence that efficiently conveys the tool's function and lists six relevant aspects. Every word contributes value, with no fluff or 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 no-parameter informational tool without an output schema, the description is remarkably complete. It enumerates all major components of the methodology, making the tool's purpose and expected content self-explanatory.

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 an empty schema (100% schema coverage), so the baseline is 4. The description adds further context by detailing the domains the methodology spans, which enriches understanding beyond the schema itself.

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: explaining 'how the index is measured,' and enumerates specific components (prompt panel, engines, scoring rules, etc.). This distinguishes it from sibling tools focused on visibility data or history, making the purpose unmistakable.

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 the tool is used when one needs methodology details behind the visibility index, but it does not explicitly state when to use it over alternatives or provide exclusion criteria. Usage context is inferred rather than stated.

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 full responsibility for behavioral disclosure. It states the data is a snapshot with 'Weekly measurement' and clearly describes the output contents. As a 'get' operation, there is no destructive side effect, and the description accurately represents what the tool returns, though it doesn't discuss potential large payload size or rate limits.

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 two sentences with no filler. It front-loads the main purpose and packs all relevant details (brands, metrics, engines, timeframe) into a compact structure. Every word adds value.

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 there are no parameters and no output schema, the description provides a complete picture of the resource: what data is included, the engines, the metrics, and the weekly measurement cadence. It is sufficient for an agent to decide if this tool fits the task and to understand the expected output.

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 vacuously 100%. The description adds meaning by explaining what the fixed invocation returns, which is sufficient for an agent to understand the result without needing parameter details. This aligns with the baseline for 0 params.

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 resource as the 'Full DABLOCK AI Visibility Index' and enumerates its contents (rank, share of answer per engine, commercial intent, quadrant). It distinguishes itself from siblings like get_brand_visibility (which likely targets a single brand) by emphasizing 'every tracked 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 provided about when to use this tool versus alternatives. The phrase 'every tracked brand' hints at a comprehensive view, but there is no direct comparison to siblings like get_brand_visibility or get_history. The agent is left to infer use cases without clear direction.

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 must carry the full burden. It discloses the scope ('all brands') and output shape ('with their slugs'), but doesn't explicitly state read-only behavior, authentication needs, or other potential traits. This is minimal but adequate for a simple list tool.

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, front-loaded sentence that conveys the core purpose with no redundant information. It is exemplary in conciseness.

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, simple list tool, the description adequately conveys the return value. A minor gap is that it doesn't link the slugs to sibling tools, but this is not critical given the simplicity.

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 parameters, meriting a baseline of 4. The description doesn't need to explain parameters, and the mention of 'slugs' refers to the output, not a parameter.

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 all brands tracked in the DABLOCK index, including their slugs. The verb 'list' and resource 'brands' are specific, and the tool is distinct from sibling tools like get_brand_visibility or 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?

No guidance is provided about when to use this tool versus alternatives, or how the returned slugs might be used with sibling tools. The description only states what it returns without any contextual or exclusionary notes.

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