dablock.ai

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

No annotations are provided, so the description carries the transparency burden. It discloses the output fields (share of answer, rank, quadrant, panel prompts) and implies read-only behavior via 'get'. It also warns about valid slugs and points to list_tracked_brands, though it doesn't state authentication or error behavior.

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 sentences, highly dense and efficient. The first sentence packs the output dimensions, and the second gives a practical pointer. No wasted words 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?

For a one-parameter tool with no output schema, the description covers the main output dimensions and provides a validation path. It could clarify output format and define terms like 'quadrant', but it is largely adequate for an agent to invoke.

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 is 100%, with the schema describing the slug and examples. The description adds value by referencing list_tracked_brands for valid slugs, providing a concrete source to find acceptable parameter values.

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 returns a single brand's AI visibility metrics, including share of answer per engine, rank, quadrant, and panel prompt mentions. This specific verb+resource scope distinguishes it from sibling tools like get_visibility_index (aggregate) and list_tracked_brands (list of 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 clearly implies this tool is for one brand's visibility data and explicitly instructs using list_tracked_brands for valid slugs, providing a prerequisite. It doesn't explicitly compare with sibling tools like get_visibility_index, but the context is sufficient.

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 discloses that data is published weekly and comparable only within one panel version, which is useful. However, it does not describe the output format, potential limitations, or whether any side effects exist (though none are expected).

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 point ('Full measurement history') and then clarifies the exact content and scope. Every word adds value, with no 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's simplicity (no parameters, no output schema, no annotations), the description is fairly complete. It specifies the data granularity (weekly, per brand), the scope (full history), and a critical constraint (panel version comparability). It could elaborate on the meaning of 'share of answer' but is sufficient for a basic understanding.

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 appropriately focuses on the data returned rather than parameters, and there is no parameter information needed beyond the empty 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 clearly specifies the tool's purpose: providing the full measurement history of share of answer per brand at each weekly published measurement. It distinguishes from siblings by focusing on historical data rather than current visibility or indices, though it lacks an explicit verb like 'retrieves' or '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?

The description provides no explicit guidance on when to use this tool instead of siblings like get_brand_visibility or get_visibility_index. The caveat about 'comparable within one panel version' implies a usage constraint but does not state when to choose this tool over alternatives.

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 burden of behavioral disclosure. It lists the content areas covered (prompt panel, engines, scoring rules, etc.), implying a read-only informational purpose. However, it does not disclose the return format or whether additional context is needed.

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 enumerates the key topics. It contains no fluff and every phrase adds value, making it an ideal concise tool description.

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 informational tool, the description adequately conveys what information is returned by listing the methodology components. It could be more explicit about the return format (e.g., a text report, structured data), but the current description is largely sufficient for an agent to understand the tool's purpose.

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. The description adds no parameter-specific detail, but the baseline for zero-parameter tools is 4 since no additional semantic contribution is required.

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. It names the resource (methodology) and distinguishes it from siblings like get_visibility_index, which likely provides the actual index value rather than the 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 usage is implied: use this tool when seeking measurement methodology details. However, there is no explicit guidance on when not to use it or comparisons to alternative tools like get_visibility_index or get_history.

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 available, the description carries the full burden of explaining behavior. It details the contents of the response (rank, share per engine, commercial intent, quadrant) and notes the weekly measurement cadence, but it doesn't explicitly state that this is a read-only operation or mention any limitations such as data volume or access restrictions.

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 packs essential information about the index contents without redundant wording. Every phrase adds value, from the list of engines to the weekly measurement note.

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 is no output schema, the description serves as the primary documentation for what the tool returns. It enumerates all the key data points (rank, share of answer per engine, commercial intent, quadrant) and the update frequency, making it sufficiently complete for an agent to invoke the tool and interpret the response.

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 confirms this with an empty properties object. Per the baseline for 0-parameter tools, the description doesn't need to elaborate on parameters; it instead focuses on the return value, which 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 clearly states that this tool retrieves the full DABLOCK AI Visibility Index, covering every tracked brand with specific metrics. It distinguishes itself from sibling tools like get_brand_visibility by emphasizing the comprehensive nature of the data.

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 phrase 'every tracked brand' implies this is for market-wide visibility rather than individual brand queries, but it doesn't explicitly state when to use this tool over alternatives like get_brand_visibility. No exclusions or alternative references are provided.

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 disclosing behavior. It states the content (brands and slugs) but does not mention whether this is a read-only operation, any output format, ordering, or access considerations. For a simple list, the implied behavior is 'return these items,' but no explicit disclosure is given.

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, compact sentence that directly conveys the tool's output. It is front-loaded and free of fluff, every word earns its place.

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 is mostly adequate but could be more explicit about the return type (e.g., 'Returns a list of...'). It does not mention whether the list is ordered, how to interpret slugs, or whether any filtering applies. For a simple list tool this is a minor gap, but it falls short of being fully self-contained.

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 is not expected to explain parameter behavior. Per the baseline rule for 0 params, a score of 4 is appropriate. The description adds no parameter-specific semantics, but none are 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 indicates the resource ('all brands tracked in the DABLOCK index') and includes a key attribute ('with their slugs'). While it lacks an explicit verb like 'lists' or 'returns', the tool name makes the action obvious. It distinguishes itself from sibling tools by specifying a unique dataset.

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 the sibling tools (get_brand_visibility, get_history, etc.). There is no mention of context, prerequisites, or alternatives. The description simply states what the tool returns.

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