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

With no annotations provided, the description carries the full behavioral burden. It discloses fuzzy matching, candidate ranking, ambiguity flagging when ties occur, and read-only access to a small dimension table with no fact scan. This gives the agent a strong safety and behavior profile.

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?

Three dense sentences deliver purpose, kind disambiguation, and behavior with no filler. The examples and parentheticals add meaning rather than clutter, and the most important information is front-loaded.

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?

For a 3-parameter tool with an output schema and no annotations, this description is nearly complete. The only notable gaps are that limit semantics and no-match behavior are not addressed, but the schema default covers limit and the overall guidance is sufficient for correct invocation.

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 0%, so the description must compensate. It explains the kind parameter's allowed values with a concrete 'Fulton' example and defines query as a name or code. The limit parameter is not described, though its default value is visible in the schema.

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 and resource: resolving place/demographic names or codes into stable keys and labels. It clearly distinguishes from siblings by warning that a wrong code guess would otherwise return an empty query_dataset page, and notes this reads only the dimension table.

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 frames this as 'the right way' to convert names/codes into filter keys before querying, and warns about the empty-result failure mode of guessing codes. It does not enumerate when to prefer siblings like describe_dimension or distinct_values, but the context is clear enough for an agent to select it.

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