dabyte.ai

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 specific output metrics (share per engine, rank, quadrant, panel prompt count), which clarifies the tool's behavior effectively. It doesn't mention rate limits or authentication, but for a read-only lookup this is adequate.

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 with no filler. The first sentence front-loads the purpose and output, the second provides a useful pointer for valid input. 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?

For a simple one-parameter tool with no output schema, the description covers the return values and input validation. It doesn't define jargon like 'quadrant' or 'panel prompts', but those are likely explained by get_methodology, making this adequately 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 is 100% (slug described with examples), so baseline is 3. The description adds value by directing users to list_tracked_brands for valid slugs, which helps parameter selection 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 states it returns 'one brand's AI visibility' with specific metrics: 'share of answer per engine, rank, quadrant, and how many panel prompts name it.' This clearly distinguishes it from siblings like get_visibility_index (aggregate index) 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?

It provides a clear use case for a single brand and directs the user to 'list_tracked_brands for valid slugs,' which is a helpful prerequisite. It doesn't explicitly exclude usage compared to get_visibility_index but the wording 'One brand's' implies the distinction.

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 disclosure. It provides a useful caveat that data is comparable only within one panel version and describes the return granularity, which adds value. However, it does not disclose potential side effects (though likely a read operation), data volume/pagination, or any access requirements, leaving room for improvement.

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 states the core purpose ('Full measurement history') followed by specifics. Every phrase earns its place, with no redundant wording or placeholder text.

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 simple, parameterless read tool with no output schema, the description covers the essential return value (per-brand share for each weekly measurement) and a key caveat (panel version comparability). It lacks details like data freshness or how 'full' history is bounded, but overall it is reasonably complete for its 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 tool has zero parameters and the schema is empty, so the baseline is 4. The description adds meaning by explaining what data will be returned, which is sufficient given there is nothing to configure.

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 resource ('measurement history'), the exact content ('share of answer per brand at every published weekly measurement'), and adds scope ('full', 'comparable within one panel version'). It distinguishes this tool from siblings like get_brand_visibility or get_visibility_index by emphasizing historical weekly data 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 implies the tool is for retrieving historical weekly measurements, which gives a clear context for when to use it. However, it does not explicitly state when not to use it or mention alternatives among the sibling tools, so the guidance is implied rather than explicit.

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 disclosing behavior. It provides an outline of content but does not state whether the operation is read-only, whether it returns a document or structured data, or any limitations. The informational nature suggests safety, but it is not explicitly stated.

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 and enumerates the specific topics covered in a clear, efficient manner. 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 there is no output schema and no annotations, the description does a good job of conveying the scope of the methodology content. It lists six distinct aspects that will be covered, which is fairly complete for an informational tool. It could add more context about the format or depth, but the current outline is sufficient for an agent to decide if this tool is relevant.

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, so the description does not need to explain parameter usage. A baseline of 4 applies, and the description focuses on what the tool returns rather than inputs, 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 the tool explains how the index is measured, listing specific topics (prompt panel, engines, scoring rules, etc.). This distinguishes it from sibling tools that focus on visibility or history. However, it lacks a verb-like action phrase, slightly reducing clarity.

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 use when an agent needs to understand the measurement methodology behind the index. It does not explicitly state 'use this when' or contrast with alternatives like get_visibility_index or get_history, but the context makes the usage somewhat evident.

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?

No annotations are present, so the description carries the burden. It discloses the exact contents (rank, share per engine, commercial intent, quadrant) and the weekly update cadence, which helps set expectations about data freshness. It does not discuss read-only behavior or auth, but the absence of side effects is implied by the nature of the 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?

Two short sentences deliver all essential information: purpose, data fields, engine scope, and measurement frequency. There is no filler, repetition, or unnecessary detail.

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 0-parameter, no-output-schema tool, the description fully covers what the agent will receive: brands, ranks, share of answer overall and per engine, commercial intent, quadrant, and weekly measurement. Nothing critical is missing for decision making.

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, so the baseline is 4. The description confirms there is nothing to configure and explains what the returned data contains, which is all that 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 states it returns the 'Full DABYTE AI Visibility Index' with every tracked brand, rank, share of answer, engine breakdown, commercial intent, and quadrant. The 'Full' and 'every tracked brand' clearly distinguish it from siblings like get_brand_visibility (single brand) and list_tracked_brands (just names).

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 this is the comprehensive data dump for a full-market view, while siblings exist for single-brand or history-specific queries. It does not explicitly name alternatives or say 'use when you need all brands,' but the context is clear enough for an agent to infer when this tool is appropriate.

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?

No annotations are provided, so the description carries the full burden. It discloses the output content (brands and slugs) but does not clarify ordering, filtering, pagination, or any read-only guarantees. For a simple list operation, this is minimally sufficient.

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, clear sentence with no superfluous content. It efficiently communicates the tool's purpose without wasting tokens.

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 simplicity of the tool—no parameters and a straightforward output—the description is contextually complete for most use cases. However, it does not explicitly state the return format (e.g., an array of objects), which an output schema would normally clarify.

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 explain parameter meanings. The baseline for 0-param tools is 4, and the description appropriately focuses on the output rather than redundant 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 clearly states the tool lists all tracked brands with their slugs, conveying a list operation through 'All brands'. It is distinct from sibling tools like get_brand_visibility or get_history, which focus on different data aspects.

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, such as 'use this for an overview'. The usage is implied but not articulated, leaving the agent to infer context.

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