email_search

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

No annotations are provided, so the description carries the full burden. It discloses a behavioral limit ('up to 5,000 results') and explicitly details the operation/argument invocation pattern. However, it does not mention return format, error behavior, or rate limits, which would be useful for a search endpoint. Still, the core behavior is transparent enough for an agent to invoke correctly.

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 compact and well-structured: a one-sentence purpose, a formatted list of operation parameters, and a clear 'When to use' line. Every sentence earns its place with no redundant or vague content.

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 tool is simple (one operation, two params). The description covers purpose, parameters, and usage guidelines, and the result limit adds useful context. However, with no output schema or annotations, a brief note on the return structure (e.g., 'returns a list of email addresses') would make it fully complete. The implied output is sufficient for most selection purposes.

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 only defines 'operation' and a generic 'args' object with no schema for the actual operation parameters. The description compensates fully by naming the operation ('search_emails') and listing the required parameters ('query, email_domain'), while also instructing that parameters go in 'args'. This is essential semantic information beyond the schema.

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 states a specific verb ('Find'), a resource ('email addresses'), and a scope ('within a given email domain') with a result limit. It also distinguishes the tool from the sibling website_contacts_scraper, making the purpose clear 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 'When to use' section explicitly states the intended use case ('discover emails by query + domain') and provides an explicit alternative ('To scrape contacts from a known website use website_contacts_scraper'). This is model guidance for selecting between tools.

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