Ninar AI

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

Annotations already state readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavioral context: dual-model verification, automatic escalation on disagreement, and the return of a confirmed/rejected decision, confidence score, reasoning, and audit trail. It also notes the 'Pro plan or higher' requirement, which is beyond 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 three sentences, each with distinct value: purpose, mechanism, and outputs/requirements. It is front-loaded with the core question and lacks redundancy or filler.

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?

Despite no output schema, the description lists the return components (decision, confidence, reasoning, audit trail) and discloses plan requirements. For a moderately complex tool with nested objects, it covers purpose, behavior, and outputs well. It doesn't cover error handling or edge cases, but annotations plus schema fill most 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 input schema covers all three parameters with descriptions, so the baseline is 3. The description adds a concrete example for the 'entity' parameter but does not elaborate on 'taxonomy_id' or 'raw_evidence' beyond the schema's definitions. No additional parameter semantics 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 states the tool's function: checking whether an AI-surfaced brand or entity is a genuine competitor. The verb 'Check whether' plus the specific resource ('brand or entity') and the example ('Banner Life' vs. Enact) distinguish it from sibling tools like scan_visibility, which likely focuses on a brand's own visibility rather than competitor adjudication.

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 gives a clear usage context: when an AI engine surfaces a brand/entity and you need to verify its competitive status. The example further anchors the scenario. However, it does not explicitly mention alternative tools or when NOT to use this tool, so it stops short of a 5.

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?

Since annotations provide no useful hints (all false), the description carries the burden. It discloses that content is returned inline (no dashboard visit required), the Pro plan requirement, and the behavior of gap_type='all'. This adds meaningful non-obvious context beyond 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 two succinct sentences: the first states the core purpose and content types, the second adds key behavioral details (inline return, plan requirement, 'all' option). Every sentence earns its place with no redundancy or fluff.

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 low complexity (one parameter, no output schema, no nested objects), the description is complete. It covers purpose, output format, access requirements, and special parameter values, so the agent has enough to invoke it correctly without additional documentation.

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 description provides complete coverage (100%) for the single parameter gap_type, including all enum values and an explanation for 'all'. The description only repeats the 'all' hint, adding no extra semantic value beyond what the schema already offers, so the baseline of 3 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's function: generating AI-optimized content (FAQ, about, use cases, differentiators) for gaps in the latest scan. The verb 'generate' and specific content types make the purpose distinct from sibling tools like list_content_gaps or scan_visibility.

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 gives clear usage context: it's for gaps in the latest scan, requires a Pro plan or higher, and provides a shortcut for getting all blocks at once (gap_type='all'). However, it doesn't explicitly mention when not to use it or name alternatives, so it falls short of a 5.

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=true and idempotentHint=true, so the safety profile is known. The description adds useful context: it requires a free Ninar account and explains the scope (most recent brand). It does not fully describe error cases, but with annotation coverage, this is 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 two sentences long, front-loaded with the primary purpose, and every clause adds value. The first sentence states what and for whom, and the second states the account requirement. No filler 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 tool with no parameters, no output schema, and simple semantics, the description covers the essential elements: the metric, its range, the scope, and a prerequisite. It could be more explicit about the response format (e.g., structured by engine), but it's adequate for a simple read operation.

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 clarifies the implicit inputs (signed-in user and most recently scanned brand), which adds meaning beyond the empty schema. This meets the baseline 4 and exceeds it by specifying implicit context.

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 a specific verb ('Get') and resource ('AI Visibility Index'), defines the numeric range (0-100), and specifies the scope ('signed-in user's most recently scanned brand') and breakdown ('by engine'). This distinguishes it from sibling tools like scan_visibility and audit_brand_visibility, which have different purposes.

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 clear context for when to use the tool: it retrieves the latest score for the user's most recent scan, and it notes the account prerequisite. However, it does not explicitly contrast with alternatives or state when not to use it, so it falls short of a 5.

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 destructiveHint, and the description adds useful context about the output being AI-generated, publishable suggestions for the signed-in user. This goes beyond the annotations without contradicting them.

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 starts with the action and efficiently packs in the resource type, scope, and source. No unnecessary 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 parameterless read-only tool with annotations and no output schema, the description covers the essentials: what is returned (a list of content suggestion types), for whom (signed-in user), and from where (latest scan). It is complete for an agent to invoke correctly.

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?

With zero parameters, the schema is fully covered, and the baseline for a parameterless tool is 4. The description adds no parameter-specific detail because there are none, but it does clarify the source (latest scan) indirectly.

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 uses the specific verb 'List' and identifies the exact resource: AI-generated content suggestions (FAQs, differentiators, use cases, about copy) from the latest scan. This clearly distinguishes it from sibling tools like generate_content or scan_visibility.

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 it should be used after a scan ('from my latest scan') and for the signed-in user, but it does not explicitly contrast with alternatives or state when not to use it. No sibling tool is mentioned, so guidance is only implied.

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?

The description adds behavioral context beyond annotations by revealing that engine count scales with the user's plan (free = 2) and listing the engines scanned. It doesn't mention side effects or rate limits, but annotations already indicate non-destructive behavior, so no contradiction.

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 concise sentences, front-loaded with the main action, with no filler or repetition. Every sentence adds useful information about the two modes and plan-scaling behavior.

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?

The description adequately explains the core behavior, including the decisive city parameter and plan-dependent scaling. However, it does not describe the output format or explicitly differentiate from the sibling audit_brand_visibility, leaving some gaps for a tool with six parameters and no 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?

Schema description coverage is 100%, so baseline is 3. The description's mention of city passing/omitting partially reflects the schema's own description of the `city` parameter, adding little new parameter-level meaning beyond what the schema already provides.

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 runs an AI visibility scan for a brand, with specific modes (local-business vs multi-engine GEO) and names the engines involved. This specific verb+resource+scope is sufficient to distinguish it from sibling tools like audit_brand_visibility.

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 gives explicit instructions on when to pass `city` vs omit it, defining two distinct use cases. However, it does not mention alternatives or when not to use this tool compared to siblings such as audit_brand_visibility, so it lacks full exclusion guidance.

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