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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds meaningful behavioral context by explaining the model is trained on historical listing asking prices, not deed-level sales, which informs the trustworthiness of the result. This is valuable context beyond the annotation flags.

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 compact and front-loaded with the core purpose. The second sentence adds a meaningful caveat about training data, and the third sentence summarizes outputs. It is slightly verbose with 'AI-powered' and the model name, but overall every sentence contributes useful information.

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 annotations, a fully documented schema, and an output schema, the description is sufficiently complete. It explains the data basis and high-level returns, and the structured fields carry the remaining technical details. No critical operational information for calling the tool appears to be 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%, so every parameter is already documented in the input schema. The description adds no additional parameter-level meaning, so the 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 description states a specific verb and resource: it provides an AI-powered price estimate for a Luxembourg property. It also clarifies the output (estimated price, confidence interval, comparable statistics), making it clearly distinct from sibling tools like get_listing or get_market_context.

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 clearly indicates this is the tool for obtaining a property valuation, and the 'estimate' wording implies it should be used when a predicted price is needed rather than raw listing or market context. It does not explicitly name alternatives or exclusions, but the context is sufficiently clear.

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