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

With no annotations, the description carries the full burden and does so richly: fuzzy matching and ranking, the `ambiguous` tie flag, that it reads only the small dimension table (no fact scan), the matched_on taxonomy (consolidated_city, typo, city_system, county_system), and the instruction to surface `place_note`. These are non-obvious behaviors an agent could not infer from the 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 purpose and the why before the behavioral detail. The matched_on examples are dense but each earns its place by disambiguating a real failure mode. It is longer than strictly necessary, with several parenthetical examples that could be trimmed.

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?

An output schema exists, so return fields need not be enumerated, yet the description still pre-empts the key output semantics an agent must act on (ambiguous, matched_on, place_note). Combined with the usage rationale, nothing an agent needs to call this correctly 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 0%, so the description must compensate. It fully documents `kind` (district/school/county/demographic with worked examples) and richly illustrates `query` semantics via name/code examples. The `limit` parameter is never mentioned, leaving one of three parameters undocumented in both places.

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 a specific verb+resource ('Resolve a place or demographic NAME or CODE to its stable keys + labels') and immediately names the concrete artifacts it produces (district_code / school_code / county_fips / demographic). It also distinguishes itself from the sibling query_dataset by explaining that a wrong code guess returns an empty page, so an agent can tell why it should call this first.

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?

Clear context for use: converting human names or raw codes into filter keys before querying, with an explicit warning that guessing codes yields empty results. It also flags the kind='county' vs kind='district' distinction. There is no explicit 'when not to use this' or a named alternative for the reverse direction, so it falls short of a 5.

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