macro_bank_english_interest_rate

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds the date range and return type but does not disclose additional traits like update frequency, data granularity, or limitations. It does not contradict the annotations, but the added behavioral context is minimal.

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 short but includes redundant repetition of the tool's purpose (title and return line both mention '英国央行决议报告') and two source URLs that are not essential for an AI agent to invoke the tool. It is front-loaded with the main purpose, but some lines could be trimmed for greater conciseness.

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 zero-parameter read-only tool, the description effectively communicates the data source, date range, return type, and measurement unit. It lacks details like index format or update frequency, but is largely complete for the tool's simplicity. The absence of an output schema is partially compensated by the explicit return description.

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 has zero parameters and the schema is empty, so the description correctly focuses on return type (pandas.Series) and value unit (%). With no parameters to document, the baseline of 4 applies, and the provided return information is sufficient.

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 tool as providing the Bank of England decision report with a data range from 1970-01-01 to present, and specifies the return as current value (%). It differentiates from sibling central bank tools by naming '英国央行' (Bank of England). However, it lacks an explicit verb like 'fetch' or 'get', making the action somewhat implied rather than directly stated.

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 guidance on when to use this tool versus alternatives such as macro_uk_bank_rate or other central bank interest rate tools. It only states the data range and source, not the recommended context or exclusions. Given the extensive sibling list, this is a notable gap.

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