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

The annotations already signal readOnly=true, idempotent=true, and non-destructive, but the description goes well beyond them. It explains what each metric means, warns that affordability percentages are country-own historical comparisons rather than cross-country rankings, discloses that mortgage rates are absent outside the euro area rather than estimated, and reveals the material up-to-40% understatement for modelled rents. This is exactly the kind of non-obvious behavioral nuance an agent needs before trusting results.

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?

Although the description is long, every sentence carries operational value: what is returned, how ratios relate, the crucial own-history caveat, the rentBasis reliability warning, and the data-coverage limits. It is front-loaded with the core purpose and then layers caveats in a logical order. No filler or redundancy is present.

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 the tool's complexity, the optional parameters, and the existence of an output schema, the description is remarkably complete. It explains return values, units, fallback behavior, data sources, limitations, and interpretation hazards. An agent has enough context to call the tool correctly, interpret results sensibly, and avoid common misreadings without needing to inspect the output 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%, so the baseline is 3 and the schema already documents every parameter clearly. The description adds some useful semantic context by tying remoteIncome to arbitrageGap and explaining the conceptual meaning of the ratios, but it does not substantially improve on the existing parameter descriptions. It reinforces, rather than extends, what the schema provides.

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 names a specific resource and verb: comparing housing cost and affordability across countries for remote workers. It clearly distinguishes this from broader sibling tools like compare_total_cost or compare_countries by focusing on rent, affordability ratios, and the remote-income lens. It also specifies concrete outputs (monthly rent, two ratios, arbitrageGap), so an agent knows exactly what this tool is for.

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: it is for remote workers evaluating housing affordability, and it explains when remoteIncome should be passed to get the arbitrageGap. It does not explicitly name alternatives or state 'use this instead of compare_total_cost,' but the housing-specific framing makes the intended use evident. There are no exclusions beyond the ECB/Eurostat coverage note, which is more data-availability guidance than usage routing.

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