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

Annotations already cover read-only, idempotent, open-world, and non-destructive behavior. The description adds meaningful context beyond that: 'Free, no account' discloses access requirements, and the promise of 'real row counts, column names, filtered cuts' clarifies what the response contains. This exceeds the annotation baseline without contradicting it.

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 somewhat lengthy but every section earns its place: scope, return contents, access, usage ordering, dataset examples, and invocation examples. It is front-loaded with the core purpose, and the examples are useful rather than 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 the output schema exists and annotations cover safety traits, the description supplies everything needed to select and invoke the tool correctly: what it lists, how it differs from twin_cities_records, when to call it, and realistic example queries. No critical operational detail 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 the single optional 'about' parameter already well-documented as a plain-words filter with examples and omission behavior. The description does not add parameter-level detail beyond the schema, so the baseline score of 3 applies.

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 opens with a specific verb—'Lists'—and a clear resource: public-records datasets for the seven-county Minneapolis-St. Paul metro. It further specifies exact return details (row counts, column names, filtered cuts, counties covered), making its purpose unmistakable and distinct from the sibling twin_cities_records tool.

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 says 'Call this FIRST to learn what can be answered, then call twin_cities_records to ask it.' This directly tells the agent when to use this tool versus the primary sibling, providing both ordering and routing guidance. Example invocations reinforce the intended use cases.

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