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

The description adds valuable context about what data is returned (verified business data, job title, company details, phone numbers, social profiles) that goes beyond the annotations. While annotations cover read-only, open-world, idempotent, and non-destructive properties, the description provides concrete information about the type and scope of data returned, which is helpful for understanding the tool's 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?

The description is perfectly concise with just two sentences that efficiently communicate the tool's purpose and return data. Every word earns its place, and the information is front-loaded with the core functionality stated immediately.

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 comprehensive annotations (read-only, open-world, idempotent, non-destructive) and 100% schema coverage, the description provides good contextual completeness by detailing the return data. However, without an output schema, the description could benefit from more specific information about response structure or format to be fully 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?

With 100% schema description coverage, the input schema already fully documents all three parameters. The description mentions email-based lookup and the types of data returned, but doesn't add specific parameter semantics beyond what the schema provides. This meets the baseline expectation when schema coverage is complete.

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 specific action ('Look up detailed person and company information'), the resource ('using an email address'), and distinguishes from siblings by specifying email-based enrichment rather than LinkedIn-based or validation approaches. It provides a comprehensive overview of what the tool does.

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 this tool (for email-based enrichment) and mentions what data it returns, but doesn't explicitly state when to choose it over 'enrich_by_linkedin' or 'validate_email'. It provides clear context about the tool's function but lacks explicit alternative guidance.

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 valuable behavioral context beyond what annotations provide. While annotations declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, the description specifies that it returns 'verified business data' and lists specific data types (email, job title, company details, phone numbers). This provides important context about the nature and quality of the returned data that isn't captured in 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 perfectly concise with just two sentences that each earn their place. The first sentence establishes the purpose and input, the second describes the output. No wasted words, well-structured, and front-loaded with the core functionality.

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 moderate complexity, rich annotations, and 100% schema coverage, the description provides good contextual completeness. It explains what data is returned (though without an output schema, more detail on the return structure would be helpful). The main gap is the lack of explicit guidance on when to choose this tool versus the 'enrich_by_email' sibling.

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 100% schema description coverage, the input schema already documents all three parameters thoroughly. The description mentions 'using a LinkedIn profile URL' which aligns with the 'linkedin' parameter, but doesn't add meaningful semantic context beyond what the schema provides. The baseline of 3 is appropriate when the schema does the heavy lifting.

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 specific action ('Look up detailed person and company information'), resource ('using a LinkedIn profile URL'), and output scope ('Returns verified business data including email, job title, company details, and phone numbers'). It distinguishes itself from sibling tools like 'enrich_by_email' and 'validate_email' by specifying LinkedIn URL as the input source rather than email.

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 this tool ('using a LinkedIn profile URL'), but does not explicitly state when not to use it or mention alternatives like the sibling 'enrich_by_email' tool. The context is sufficient to understand the primary use case, but lacks explicit exclusion guidance.

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 indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context by specifying what is validated (deliverability, format, risk level) and what details are returned (catch-all status, mail server info), enhancing transparency beyond the annotations without 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?

The description is front-loaded with the core purpose and efficiently lists key validation aspects and return details in two sentences. Every sentence adds value without redundancy, making it appropriately sized and 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?

Given the tool's moderate complexity, rich annotations, and no output schema, the description provides sufficient context on what the tool does and returns. However, it could improve by detailing output structure or error handling to fully compensate for the lack of 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%, with the single parameter 'email' well-documented in the schema. The description does not add significant meaning beyond the schema, such as format examples or validation specifics, so it meets the baseline for high schema coverage.

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 specific action ('validate an email address') and the resources involved ('deliverable, valid format, risk level'). It distinguishes from sibling tools like 'enrich_by_email' by focusing on validation rather than enrichment, making the purpose explicit and differentiated.

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 usage for email validation purposes but does not explicitly state when to use this tool versus alternatives like 'enrich_by_email' or 'enrich_by_linkedin'. No guidance on prerequisites or exclusions is provided, leaving usage context somewhat vague.

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