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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavior beyond annotations: it states the response includes the true matching row count, up to six example rows, and a link to the complete file. It also clarifies that the tool works over joined public records, which helps set expectations about data scope.

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 front-loaded with the core purpose, then gives a compact behavioral summary and useful example queries. It is longer than minimal but each section serves a purpose—especially the examples, which make the tool's usage concrete. It could be tightened slightly, but there is no wasted 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?

Given five parameters, an output schema, and safety annotations, the description covers the main ambiguities: how to target one property vs. the market, what response to expect, and what kinds of questions are answerable. It does not describe the within_ft parameter directly, but the schema covers that, and the half-mile example implies radius usage. Overall, an agent has enough to invoke the tool correctly.

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 schema already documents all five parameters. The description goes beyond the schema by explaining the address parameter's optionality and disambiguation rule (include the city for common street names), and by illustrating radius-based queries. This adds practical meaning that the bare schema entries do not fully convey.

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 answers questions about the Minneapolis-St. Paul metro from joined public records, with concrete examples of property sales, ownership, contamination files, and census data. It is clear what the tool does, though it does not explicitly differentiate itself from sibling tools like data_source_atlas or twin_cities_datasets.

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 guidance on how to scope queries: provide an address for one property and its surroundings, or omit it for the whole market cut. Example invocations illustrate appropriate use. It does not, however, explicitly say when to use this tool instead of a sibling or mention prerequisite steps such as calling twin_cities_datasets first.

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