qobrix_days_on_market

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

With no annotations to fall back on, the description discloses the join, computation, defaults for kind and listing_date_field, the per-row fallback chain, valid group_by fields, outlier behavior, and example combinations. This gives a complete picture of how the tool behaves beyond the raw schema.

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?

Front-loaded with a one-sentence purpose, then dense, purposeful details: defaults, fallback, grouping, outliers, and examples. Every sentence adds information or practical guidance; nothing is wastefully verbose.

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?

Without an output schema, the description explains the aggregate metrics returned and mentions outlier deals, which covers the main return behavior. It stops short of specifying output ordering or pagination, but for an aggregation tool this is reasonably 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 covers 100% of parameters with descriptions, so baseline is 3. The description adds practical value by showing realistic parameter combinations (e.g., { kind: 'sold', year: 2026, group_by: 'city', include_outliers: true }) and reinforcing the meaning of group_by and include_outliers.

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?

States 'Days-on-market analytics' with explicit formula (close_date - listing_date), join logic, and aggregation metrics (count/mean/median/p75/p90/min/max). Clear verb+resource and distinct from sibling analytics tools like funnel or timeseries.

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?

Provides four concrete example uses (overall DOM, by property type, by city with outliers, reservation cycle) that clearly convey when to call this tool. Does not explicitly name alternatives or exclusion criteria, but the context is strong.

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