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

Annotations already indicate readOnlyHint=true. The description goes beyond by specifying the exact data sources and types (live data, sale/rent prices, listing counts, Airbnb occupancy, local context), giving agents a clear behavioral model without contradiction.

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 two sentences with no wasted words. The most important information (what the tool provides and when to use it) is front-loaded, making it efficient for agent parsing.

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 simple structure (3 parameters, no output schema, read-only), the description thoroughly explains the return data (sale/rent prices, listing counts, Airbnb occupancy, local context), which is sufficient for agents to understand the tool's output without an 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 67% (2 of 3 parameters have descriptions). The description does not add parameter-level details beyond the schema, such as clarifying the country parameter's role. With high coverage, baseline 3 is appropriate; no extra value from description.

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 clearly states it provides grounded, live data about neighborhoods (sale/rent prices, listing counts, Airbnb occupancy, local context), and includes explicit usage guidance for common queries. It distinguishes itself from sibling tools by focusing on neighborhood quality assessment.

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 explicitly tells agents when to use this tool: for queries like '¿cómo es <barrio>?' or 'is X a good area?', and instructs to use it instead of answering from memory. This provides clear context and exclusions.

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