Search the FINMA registry of supervised entities and the FINMA warnings list by name. Returns up to top_k authorised entities + any matching warning entries. Use this for basic counterparty KYC screening.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Entity name (or substring) | |
| top_k | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses behavior by stating it returns up to top_k authorised entities plus matching warning entries, which is the core output behavior. It doesn't mention error cases, rate limits, or authentication, but for a simple search tool this is adequate.
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 sentences, no fluff. The purpose and return behavior are covered in a compact, front-loaded manner. Every word earns its place.
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 has no output schema, so the description's mention of return content (authorised entities + warnings) is helpful but incomplete. It doesn't specify what fields or attributes are present in results, which could be useful for KYC screening. While the tool is relatively simple, more detail about the result structure would improve completeness.
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 coverage is 50%, with 'name' described and 'top_k' only having schema constraints, not a description. The description adds meaning by explaining that top_k limits the number of authorised entities returned, thus compensating for the schema gap. It doesn't elaborate on name matching beyond 'by name' but the schema already says 'Entity name (or substring).'
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 names a specific action and resource: searching the FINMA registry of supervised entities and the warnings list. It clearly distinguishes from siblings by mentioning both registry and warnings, and explicitly ties to KYC screening.
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: 'for basic counterparty KYC screening.' This implies when to use it, though it doesn't explicitly contrast with alternative search tools like finma_search or statent_lookup. The mention of 'basic' hints at limitations but lacks explicit exclusions.
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.
finma_search and kyc_check overlap heavily; both search the FINMA registry by name, and kyc_check essentially does what finma_search does with include_warnings=true. The other tools are distinct, but this pair creates real selection ambiguity.
Names mix verb-first (classify_text), noun-first (finma_search, tariff_lookup), and pure nouns (cross_walk, entity_history). All are lowercase with underscores, so it's readable, but the inconsistent verb placement breaks a predictable pattern.
9 tools is well-scoped for a server covering multiple Swiss data domains (classifications, FINMA, tariffs, statistics). Each tool serves a distinct purpose and earns its place without being overwhelming.
The set covers core workflows: text classification, code mapping, entity search/history, tariff lookup/changelog/search, and statistics. Minor gaps like a direct get-entity-by-UID endpoint or more granular statistics exist, but agents can work around them.
