swiss_snb_financial_data

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not reveal what happens with a vague query like 'monetary policy' vs a specific one, whether results return raw data or processed numbers, whether an API key is required, or how results are structured. The description makes a broad promise ('financial and monetary data') without disclosing query limitations or edge cases.

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?

Single sentence, front-loaded with the tool's purpose and immediately lists data categories. Zero waste. Slightly generic wording ('financial and monetary data') could be tightened, but structurally it is efficient.

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?

Given the tool has one parameter with full schema coverage and no output schema, the description covers the essentials (what data categories are available). However, for a natural-language free-text query tool it lacks guidance on query phrasing, expectations for result scope, or any limitation note. Adequate for a simple single-param tool but leaves room for the agent to guess about query behavior.

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 100% and the single 'query' parameter has a helpful example set ('exchange rates EUR/CHF', 'interest rates', 'inflation', 'monetary policy') embedded in its schema description. The tool description also enumerates categories which aligns with the query parameter. However, the description does not explain how the query maps to SNB datasets — e.g., whether queries are free-text matched or must match specific SNB dataset names — which is the key meaning gap for a natural-language query parameter.

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 ('Get') with a clear resource ('Swiss National Bank (SNB) financial and monetary data') and lists concrete data categories (exchange rates, interest rates, balance sheets). It distinguishes itself from sibling Swiss tools by its domain focus, though the sibling tools are clearly different domains so differentiation is easy. A solid clear purpose statement.

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 does not provide any when-to-use guidance, exclusions, or alternatives. With a large sibling set of 36 Swiss data tools, there is no hint about when to choose this over related financial tools (e.g., swiss_energy_data, swiss_grid_energy_data). No context about prerequisites like API key setup (which appears to exist via swiss_set_api_key), leaving uncertainty about whether setup is needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.