radveo

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

With no annotations, the description carries the full burden, and it does meaningful work: it explicitly states the tool does not query an engine live, scores from the business's own site, and never guarantees placement. This discloses key behavioral limits and expectations beyond a generic 'assess' definition.

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 front-loaded with the core purpose, then adds the most important behavioral caveat, then sets expectations about guarantees. Every sentence earns its place and there is no redundant or filler wording.

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 an output schema and fully described optional parameters, the description covers the essential decision context: what it measures, how it measures it, what it returns, and its limits. The only real gap is not addressing the sibling tool or providing explicit selection guidance, which is already captured under usage guidelines.

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 the parameter schema already documents website, location, and business_name including optionality and accuracy trade-offs. The description adds context about scoring from the business's own site but does not materially change 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 names a specific action ('Score how ready a local business is'), a specific resource ('AI engines... recommendation'), and a concrete deliverable ('free, shareable Radveo report with exact fixes'). It also distinguishes itself from a generic guide by clarifying it evaluates readiness from the business's own site rather than querying an engine live.

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 when to use it (when a local business wants to know its AI-citation readiness) but does not explicitly address when not to use it or how it differs from the sibling tool 'how_to_appear_in_ai_search'. No alternative or exclusion is mentioned, leaving the agent to infer routing.

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 of behavioral disclosure. The verb 'Get' implies a read-only, non-destructive operation, which is a positive signal. However, the description does not explicitly state whether the tool has any side effects, requires authentication, or has rate limits. It also does not mention the format of the returned playbook, though an output schema exists. The disclosure is adequate but not comprehensive.

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 fronts the key information: what the tool provides and for whom. It avoids unnecessary detail and is immediately actionable. Every clause contributes to understanding the tool's purpose, making it concise and effectively structured.

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 tool with an output schema, the description is complete. It clearly states what the tool returns (a playbook with AEO steps), the target audience (local business owners), and the scope (AI engine answers). Since an output schema exists, the description does not need to explain return values. The tool is simple and the description covers all necessary context for an agent to invoke it 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?

The tool has zero parameters, and the input schema is an empty object. The schema description coverage is trivially 100%, and with no parameters to document, the description does not need to add parameter semantics. According to the baseline rule for 0 params, a score of 4 is appropriate; no additional parameter information 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 clearly states the tool's purpose: 'Get Radveo's honest, practical playbook for getting a local business found and cited inside AI engine answers.' It uses a specific verb ('Get') and a specific resource ('playbook'), and it names the target AI engines. This distinguishes it from the sibling tool 'check_ai_visibility' which presumably checks visibility rather than providing a how-to guide.

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 the sibling 'check_ai_visibility'. It does not mention any alternatives or exclusion criteria. The only implied usage is 'if you want the playbook, use this', but there is no direct comparison or context to help an agent choose between the two tools.

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