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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false. The description adds value beyond that by explaining that real values are CPI-deflated and that the response contains indexed time series with period/value pairs. This is useful behavioral context without contradicting any annotation.

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 three sentences with no wasted words. The core action is front-loaded, the deflation detail is a useful second sentence, and the return format closes it out. 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?

Given that an output schema exists, a 100%-covered input schema, and rich annotations, the description covers the essential context: data source, index type, inflation adjustment, and return shape. It doesn't explicitly explain the multiple-time-series aspect or max_series semantics, but those are already in the schema, so the description is sufficiently complete.

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%, with each parameter (country, end_period, max_series, value_type, start_period) already having a descriptive schema entry. The description mentions nominal vs. real (which maps to value_type) but adds no meaning beyond what the schema already states, so the baseline 3 applies.

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 opens with a specific action and resource: 'Fetch a country's selected residential property price index... from the BIS quarterly property price series (WS_SPP)'. It names the exact dataset (WS_SPP), the unit of analysis (residential property prices), and the nominal/real dimension. It clearly distinguishes itself from BIS sibling tools (exchange_rates, policy_rates) and the broader ecosystem of economic indicators.

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 a clear use case ('used to track housing market cycles and financial stability risk') and states the data's cross-country comparability, which signals when to pick this over country-specific property series. It stops short of explicitly saying when not to use it or naming alternatives, so it misses the top score.

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