screen_emailScreen an email address across the enabled sanctions and crime lists.
lists: optional comma-separated list slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | |||
| lists | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds that the screening applies across 'enabled sanctions and crime lists.' This adds context but does not disclose behaviors like default list selection when `lists` is omitted, or what the response contains.
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 two sentences long, with the purpose stated up front and the parameter note in the second sentence. No unnecessary words or repetition.
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 description covers the primary action and one parameter, but it lacks important details about the screening behavior, such as the meaning of 'enabled' lists or the default behavior when no `lists` are supplied. The existence of an output schema mitigates the need to describe return values, but other contextual gaps remain.
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 0%, so the description must compensate. It explains the `lists` parameter as 'optional comma-separated list slugs,' giving it meaningful semantics, but it leaves the `email` parameter without additional guidance beyond its type.
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 the specific verb 'screen' and identifies the resource as 'email address', also adding the scope 'across the enabled sanctions and crime lists.' This clearly states what the tool does, but it does not explicitly reference sibling tools or exclude alternatives, so it falls short of full differentiation.
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 its use case—screening email addresses—but provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternative tools such as screen_crypto_address. The phrase 'across the enabled sanctions and crime lists' gives some context but leaves ambiguity about what 'enabled' means.
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.
Each tool targets a unique entity type: IP, country, crypto address, email, government ID, PEP, and website. The two IP-related tools (detect_vpn and geolocate_ip) are distinct in purpose and clearly named. No two tools overlap in what they screen.
The eight screen_* tools follow a consistent verb_noun pattern (e.g., screen_email, screen_website). The two IP tools (detect_vpn, geolocate_ip) use different verbs, creating minor inconsistency but still understandable given their unique actions.
With 8 tools, the server is well-scoped for a sanctions/anti-crime screening service. Each tool addresses a distinct screening domain, and the count is neither too thin nor bloated.
The tool set covers a wide range of entity types: IPs, countries, crypto, email, government IDs, PEPs, and websites. A minor gap exists for phone numbers or other entity types, but the primary screening workflows are well-covered and usable without workarounds.