Check an email address

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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently reports quota consumption, explains canonical alias collapsing behavior, and notes specific return fields like reject_reason. It doesn't explicitly state read-only nature or error handling, but these are reasonably implied by the enrichment context.

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 moderately detailed but each sentence adds value: purpose, canonical key, specific fields, and quota. The structure is logical, though the first sentence is dense with colon-separated lists. It is appropriately sized for a tool with no output schema.

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?

Since there is no output schema, the description is the sole source for return values. It lists the main fields (canonical, is_aliased, alias_tricks, reject_reason) and covers quota consumption. It could be more complete by detailing response structure or error cases, but it covers the key behavioral and output aspects for a check tool.

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 schema only describes the parameter as 'The email address to check' with type string. The description adds that the tool accepts one email address (not a batch) and explains the canonical key semantics, providing meaningful context for interpreting the parameter. It stops short of providing format constraints or examples.

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 identifies the tool as an email enrichment and reputation lookup, enumerating specific outputs such as disposable domain, deliverability, fraud score, and abuse history. It distinguishes itself from sibling tools like check_ip and check_phone by explicitly focusing on email addresses.

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?

Provides a clear use case: using the canonical key to compare across accounts to detect duplicate signups. It also mentions quota consumption as a cost consideration. However, it doesn't explicitly state when not to use this tool or reference alternatives beyond the implicit sibling context.

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