macro_uk_bank_rate

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the source URL and return type (DataFrame), but does not disclose other behavioral traits such as data update frequency or potential network delays. With annotations present, this is adequate but not rich.

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 extremely concise, consisting of a title-like line, a source URL, and two docstring lines. Every element is purposeful with no redundancy, appropriate for a zero-parameter data retrieval tool.

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 provides basic context: the source, the subject (UK central bank rate decision), and the return type. However, it lacks details about the DataFrame's columns or the exact nature of the data, and there is no output schema to compensate. Given the simplicity of the tool, this is adequate but leaves room for improvement.

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?

The tool accepts zero parameters, so there are no parameter semantics to explain. The description correctly omits parameter details, aligning with the baseline of 4 for parameterless tools.

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 clearly identifies the subject as UK central bank rate decisions from Eastmoney economic data, and the :rtype: pandas.DataFrame indicates data retrieval. It distinguishes from sibling macro tools by explicitly naming the UK, though it lacks an explicit action verb like 'get' or 'return'.

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 provides no explicit guidance on when to use this tool versus alternatives. It is implied from the name and title that it relates to UK bank rate, but there is no mention of when to prefer this over sibling tools like macro_bank_english_interest_rate or macro_uk_cpi_yearly.

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