organization_enrich

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. While it mentions what data is returned, it doesn't describe important behavioral aspects like rate limits, authentication requirements, error conditions, data freshness, or whether this is a read-only operation. The description is functional but lacks operational transparency needed for an agent to use it effectively.

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 efficiently organized: purpose statement, data scope, parameter documentation, return value, and example. Every sentence serves a clear purpose with zero redundancy. The information is front-loaded with the core functionality stated first, making it easy for an agent to parse quickly.

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 has an output schema (which handles return value documentation) and only one parameter, the description provides adequate context for basic usage. It covers purpose, parameter meaning, and data scope. However, for a data enrichment tool with no annotations, it should ideally mention more about behavioral aspects like rate limits, data sources, or accuracy considerations 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 0% schema description coverage and only one parameter, the description provides essential semantic context that the schema lacks. It explains that the 'domain' parameter should be a company domain like 'stripe.com' and provides a concrete example. This adds meaningful value beyond the bare schema, though it could elaborate on format requirements or validation rules.

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 an organization's profile by domain' with specific details about what data is retrieved (firmographics, technographics, funding info). It distinguishes from sibling tools like 'organization_search' by focusing on enrichment rather than searching. However, it doesn't explicitly contrast with 'people_enrich' which serves a similar enrichment function for individuals.

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 context through the example and parameter description, suggesting it should be used when you have a company domain and need comprehensive organizational data. However, it doesn't explicitly state when to use this versus alternatives like 'organization_search' or 'people_enrich', nor does it mention any prerequisites or exclusions for use.

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