FBI NIBRS Crime Data

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

No annotations are present, so the description carries the full burden. It discloses that offense counts are typically higher than incident counts and mentions the limit, but lacks details on auth, rate limits, or side effects. The description is adequate but not exhaustive.

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 concise with a clear one-sentence purpose followed by parameter details. Every sentence adds value, and it is front-loaded with the core functionality.

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 tool has an output schema and four parameters, the description covers the key aspects: purpose, parameter meanings, and the offense vs. incident distinction. It lacks edge cases or examples but is complete enough for typical use.

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%, but the description fully compensates by explaining all four parameters: crime_category (with values 'person'/'property' and default), indicator, domain, and limit (with default and max). This adds essential meaning beyond the bare 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 clearly states the tool returns NIBRS offense-level estimates for person or property crimes, contrasting with incident-level data, which distinguishes it from sibling tools like get_property_incident_estimates.

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?

It explains that offense-level data differs from incident-level and provides defaults, but does not explicitly specify when to use this tool over alternatives, leaving some implicit guidance.

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

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

No annotations are provided, and the description does not disclose any behavioral traits beyond what the tool returns (weighted estimates). It lacks information about required authentication, rate limits, data freshness, or side effects, which are not critical for a read-only tool but could be useful.

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 concise with a clear two-sentence introduction followed by a structured parameter list. It front-loads the tool's purpose and provides necessary details without unnecessary repetition.

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 tool's simplicity (3 optional parameters, output schema exists), the description adequately covers the input parameters and general output. It could benefit from a brief note on typical use cases or data source, but it is mostly complete.

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?

The description provides detailed explanations for each parameter in the Args section, including examples for 'indicator' and 'domain' and constraints for 'limit' (default 50, max 1000). Since the schema has no parameter descriptions (0% coverage), this adds significant value.

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 clearly states it returns national estimates of property crime incidents from NIBRS, listing specific crime types (burglary, larceny-theft, etc.). The tool name and description distinguish it from siblings like get_violent_incident_estimates (violent crimes) and get_offense_estimates (general offenses).

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 implies usage for property crime estimates but does not explicitly state when to use this tool versus siblings or when not to use it. No alternative tools or exclusions are mentioned.

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

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

With no annotations provided, the description carries full burden. It indicates a read operation ('Get', 'Returns') but does not disclose potential safety concerns, data freshness, rate limits, or required authentication. The behavioral details are minimal but not misleading.

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 concise: two sentences for purpose and a bullet-style list for arguments. It is front-loaded with the primary purpose and avoids unnecessary verbosity, making it easy for an agent to parse quickly.

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 tool has a moderate complexity (3 optional parameters, no enums, an output schema), the description covers key aspects: what data is returned and how to filter. It does not explain output schema details (but that is handled by the output schema) or pagination beyond limit. Overall adequate for agent use.

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?

The schema has 0% coverage (no descriptions in schema), so the description must compensate. It clearly explains each parameter: 'indicator' filters by offense type, 'domain' by analysis domain, and 'limit' by maximum results with defaults and max. This adds essential meaning beyond the schema titles.

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 explicitly states it retrieves 'NIBRS victimization counts and percentages' and 'victim-level data from NIBRS including demographics, victim-offender relationships, and injury types.' This clearly distinguishes it from sibling tools like 'get_offense_estimates' and 'get_violent_incident_estimates' which focus on offense or incident estimates.

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 provides a usage hint ('Useful for understanding who is affected by crime') but does not explicitly state when to use this tool over alternatives or when not to use it. There is no mention of prerequisites or exclusions compared to sibling tools.

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

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

With no annotations, the description carries full burden. It discloses that data is weighted national estimates with breakdowns by weapon, location, etc. However, it does not state whether the operation is read-only or mention rate limits or authorization requirements. The behavior is well-described overall.

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 structured with a clear summary and an Args section, making it easy to scan. It is slightly verbose but each sentence adds value. No redundant information.

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 three optional parameters and an output schema (not shown), the description covers the return content (breakdowns) and parameter semantics adequately. It does not mention edge cases or error handling, but is sufficient for typical use.

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?

The description compensates for the 0% schema coverage by thoroughly explaining each parameter: indicator with examples, domain with a list of options, and limit with default and maximum. This adds significant meaning beyond what the raw schema provides.

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 clearly specifies the verb ('get') and resource ('national estimates of violent crime incidents from NIBRS'), and explicitly mentions it covers crimes against persons like aggravated assault, homicide, etc. This differentiates it from sibling tools like get_property_incident_estimates, which focus on property crimes.

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 implies when to use the tool (when needing national violent crime estimates) but does not explicitly state when not to use it or provide direct comparisons with siblings. It provides clear domain and indicator options, which helps guide usage.

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