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

Annotations already declare the tool read-only, idempotent, and open-world, so the description does not need to restate safety. It adds valuable behavioral context beyond the annotations: the exact output series, that values are year-over-year percentage changes, the default window, and the ability to request historical ranges. It also notes the monthly publication cadence and attribution requirement, which are useful for an agent.

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 a dense, multi-sentence block that leads with the core function, then lists series, defaults, historical behavior, and sourcing. No sentence is filler, though it is slightly long. It is still well-structured and easily parsed.

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?

With an output schema, read/idempotent annotations, and 100% parameter coverage, the description supplies all remaining operational context: what the series are, how to narrow or extend the date range, the monthly frequency, the policy relevance, and source attribution. Nothing an agent needs to choose and call this tool correctly is missing.

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%, with each parameter already documented. The description repeats the default of 12 observations and mentions start_date/end_date for ranges, but it does not add significant meaning beyond what the schema provides. This aligns with the baseline of 3 for high schema coverage.

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 states a specific verb and resource ('Get Canadian Consumer Price Index (CPI) inflation measures') and spells out exactly which four series are returned, including their codes and definitions. This is unambiguous and lets an agent distinguish it from general BoC data tools or other countries' CPI tools at a glance.

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 usage context, such as the default of 12 monthly observations and the use of start_date/end_date for history, and notes these are key measures for the Bank of Canada's 2% inflation target. However, it never explicitly mentions when not to use this tool or names an alternative among the many sibling inflation tools (e.g., bankofcanada.rates.policy, abs.economy.cpi, bls.macro.cpi), so the when-versus-alternatives guidance is only implied.

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