Bank of Canada Policy & Prime Rate

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds significant behavioral context beyond that: it returns the 10 most-recent observations by default, supports historical ranges back to 1994, explains that rate changes occur about 8 times per year on scheduled dates, and notes open-data attribution requirements. This enriches the agent's understanding of what to expect from a call.

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?

Five sentences with no filler. It front-loads the core purpose, then efficiently adds series identifiers, scheduling behavior, default return, historical depth, use case, and source attribution. Every sentence 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?

An output schema exists, so return values need no explanation. The description covers what data is returned, how to get recent vs historical observations, how often rates change, the use case, and the source/attribution requirement. An agent has everything needed to select and invoke this tool correctly without opening the schema.

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 description coverage is 100% — every parameter (recent, start_date, end_date) is already fully documented with defaults, constraints, and format examples. The description mostly reinforces what the schema states (e.g., 'Returns the 10 most-recent observations by default; use start_date/end_date for historical ranges'), so it adds only marginal meaning beyond the schema. Baseline of 3 is appropriate.

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 opening sentence states a precise action and resource: 'Get Bank of Canada overnight target rate and prime lending rate history.' It then specifies each series by ID and name (overnight rate, prime lending rate), making the tool's scope unmistakable and separately distinguishable from siblings like bankofcanada.fx.rates and bankofcanada.rates.inflation.

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 gives clear context for use: 'Essential for Canadian mortgage, loan, and bond analysis.' It also clarifies the data source and update schedule, implying when historical vs recent data is appropriate. However, it does not explicitly name sibling alternatives or state when NOT to use this tool, so it stops short of full exclusion guidance.

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