get_hate_crime_data

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

No annotations are provided, so the description carries the full burden, and it steps up: it explains the _partial_years flagging behavior and that rates are unweighted averages of available months, which is meaningful edge-case disclosure. It omits auth, rate limits, and default-vs-explicit behavior on some fields, keeping it short of a 5.

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-loads the one-line summary, then a clean Args block; each entry earns its place. Slightly verbose in a few spots (e.g., repeating default semantics), but no filler or redundancy.

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 an 8-param tool with an output schema and no annotations, the description covers formats, conditional requirements, defaults, edge cases, and a reference-data pointer. What remains missing is confirmation of return structure beyond the short summary, but the output schema covers that.

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, and it does: every parameter gets meaning, formats (mm-yyyy), conditional requirements (state required when level='state'), example values for bias codes with a lookup tool pointer, and an explicit cross-parameter constraint (aggregate applies only when data_type='counts').

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 and resource ('Get hate crime statistics') and enumerates the returned data domains (incidents, victim types, offense types, offender demographics, locations). It does not explicitly contrast with siblings like get_nibrs_data or get_arrest_data, so sibling differentiation is implied rather than stated.

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?

Usage is implied through parameter descriptions (e.g., required state/ori per level, bias lookup pointing to get_reference_data). There is no explicit when-to-use versus when-to-use-instead guidance relative to the many similarly-scoped crime-data siblings.

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