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

Beyond the readOnly/openWorld annotations, it discloses the pricing model ($0.002 USDC per successful call via x402 on Base, Solana or Arc), that failed calls are not charged, and that data freshness trails publication. These are exactly the operational facts an agent needs and cannot get from structured fields.

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 dataset identity and field list are front-loaded and the sentences are dense with substance. It is slightly overlong in the pricing clause, but no sentence is filler.

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?

With an output schema present, return values need no explanation, and the description covers source, freshness, ordering, filtering and cost. Only a hint about what the sale-price fields represent (e.g. nominal vs market transfers) is 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 coverage is 100% with per-parameter descriptions for limit and borough, so the schema carries the load. The description only echoes 'Optional borough filter' and adds nothing on the limit parameter or value formats, which is the correct baseline 3.

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 dataset (NYC Department of Finance Citywide Rolling Calendar Sales), enumerates the returned fields, and states ordering ('Newest first'), which cleanly distinguishes it from listing-scraper siblings like realestate_zillow or realestate_redfin_property_listings.

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?

It conveys the useful context that the data lags the city's publication schedule and that borough filtering is optional, but it never says when to choose this over a sibling like realestate_zillow nor any exclusions. Usage is implied rather than stated.

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