Validate an email address: syntax, domain, MX, disposable and role flags. Deep runs a live mailbox verification.
| Name | Required | Description | Default |
|---|---|---|---|
| deep | No | Include the nested deep object with richer fields. Paid on most endpoints. | |
| Yes | Email address to validate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, so the operation is known to be read-only. The description adds valuable behavioral context by explaining that the deep parameter triggers a live mailbox verification, which is a meaningful operational detail beyond the schema. It also lists the validation checks, giving a clear picture of what happens during execution.
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 extremely concise, consisting of two short sentences that immediately state the purpose and the key behavioral nuance of the deep parameter. Every word adds value, with no redundancy or filler.
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?
For a simple 2-parameter read-only tool, the description covers the purpose, the validation checks, and the deep behavior. It does not describe the return format, but with no output schema and the tool's straightforward nature, this is a minor omission. Overall, the description is sufficiently complete for an agent to understand what the tool does.
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%, so parameters are documented. The description adds extra meaning to the deep parameter by clarifying that it performs a live mailbox verification, which goes beyond the schema's vague 'richer fields' note. This enriches understanding of the parameter's purpose.
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 uses a specific verb 'Validate' with the resource 'email address' and enumerates the specific checks performed (syntax, domain, MX, disposable, role flags). This clearly distinguishes it from sibling tools like domain or mx that only perform individual lookups.
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 as the comprehensive email validation tool, but it does not explicitly state when to use it versus alternatives like domain or mx, nor does it provide exclusion criteria. The guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, e.g., city, city_search, and city_nearest are clearly differentiated by exact name, prefix search, and coordinates. However, point (reverse geocoding) returns the nearest city alongside other data, which overlaps somewhat with city_nearest, and postal includes elevation, overlapping with elevation.
All tool names follow a consistent pattern: lowercase with underscores, using noun or noun_noun combinations (e.g., city_search, country_states, postal_distance). No camelCase or mixed conventions are present.
With 31 tools, the count exceeds the 25+ threshold for 'too many'. While each tool covers a distinct data type, the sheer number makes the surface feel heavy and potentially overwhelming for an agent to navigate.
The toolset provides comprehensive coverage for a general lookup/parsing API, including CRUD-like lookups for cities, countries, states, districts, postal codes, currencies, and many other entities. There are no obvious dead ends or missing operations for the stated domain.
