screen_government_idScreen a government-issued ID (passport, tax ID, …) across the enabled lists
(fuzzy matching). lists: optional comma-separated list slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| lists | No | ||
| government_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=true already declaring safety, the description adds meaningful behavioral detail: fuzzy matching and the enabled-lists mechanism. These traits are not inferable from annotations or schema, although result formatting is not described; however, the output schema covers that information.
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?
Two compact, front-loaded sentences deliver the core purpose and parameter behavior with no filler or repetition. Every word adds value.
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 two-parameter tool with read-only annotations and an output schema, the description fully covers purpose, matching behavior, and parameter semantics. No critical operational detail is missing given the tool's complexity.
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 explicitly clarifies `lists` as optional comma-separated slugs and effectively defines `government_id` through the tool description and examples (passport, tax ID), providing semantic meaning beyond the raw 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?
Clearly states it screens a government-issued ID and gives concrete examples (passport, tax ID). This distinguishes it from sibling screening tools like screen_email or screen_crypto_address by naming the specific entity type.
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 explains the tool screens across enabled lists and documents the optional `lists` parameter, giving clear context on when to use it. It does not explicitly contrast with sibling screening tools, but the purpose is specific enough to avoid ambiguity.
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.