Fuzzy search the FINMA registry by name (tolerates typos and legal-suffix variants like 'UBS Switzerland AG' vs 'UBS AG'). Returns top-K matches with confidence score, including LEI/UID where available. Set include_warnings=true to also surface entries from the FINMA warnings list.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Entity name (or partial / mistyped name) | |
| top_k | No | ||
| include_warnings | No | Also search the FINMA warnings list |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses return format (top-K matches, confidence score, LEI/UID) and the optional warnings list behavior. However, it does not explicitly state read-only status or any limitations beyond the schema's top_k max.
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, front-loaded with purpose. Every sentence adds useful information with no redundancy.
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 tool with no output schema, the description adequately explains return values, main parameter, and optional flag. It does not mention no-results behavior, but that is a minor gap for a search 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?
Schema covers name and include_warnings descriptions but omits top_k. The description's 'Returns top-K matches' clarifies top_k's purpose, and it also reinforces name and include_warnings semantics, adding value beyond the 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?
The description uses a specific verb ('fuzzy search') and resource ('FINMA registry') with name-based scope. It distinguishes from sibling tools like statent_lookup and tariff_lookup by clearly targeting the FINMA registry and emphasizing fuzzy matching.
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 clear context for when to use: searching by entity name, tolerating typos and legal-suffix variants. It implies use for approximate name matching, but does not explicitly name alternatives or exclude exact-match scenarios.
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.
