people_enrich

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 explains what the tool does (enriches profiles with comprehensive data) and mentions the data source (Apollo), but doesn't disclose important behavioral traits like rate limits, authentication requirements, data freshness, or error conditions. It adequately describes the core functionality but lacks operational details.

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 well-structured and appropriately sized. It begins with the core purpose, then provides usage requirements, parameter explanations, return value description, and concrete examples. Every sentence serves a clear purpose with zero waste, making it easy for an AI agent to parse and understand.

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 (6 parameters, no annotations, but has output schema), the description is mostly complete. It explains the purpose, usage patterns, parameters, and return values. The output schema existence means the description doesn't need to detail return structure. However, it could better address behavioral aspects like error handling or data limitations given the lack of annotations.

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 0% schema description coverage (schema only provides titles like 'Email', 'First Name'), the description adds significant value by explaining parameter semantics. It clarifies that email is the 'best identifier', provides context for domain ('e.g., "google.com"'), and explains how parameters work together ('Provide at least one identifier...'). However, it doesn't fully explain all 6 parameters' relationships or constraints beyond the basic groupings.

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: 'Enrich a person's profile with additional data from Apollo.' It specifies the verb ('enrich'), resource ('person's profile'), and data source ('Apollo'), distinguishing it from sibling tools like people_search (which searches rather than enriches) and organization_enrich (which targets organizations instead of people).

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 explicit usage guidelines: 'Provide at least one identifier (email, LinkedIn URL, or name + company).' It also offers specific examples of when to use different parameter combinations (enrich by email vs. enrich by name + company), giving clear guidance on how to invoke the tool effectively.

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