FBI NIBRS Crime Data

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

No annotations provided, so description carries full disclosure burden. It successfully explains the semantic behavior of the data (offense aggregation logic and why counts differ from incidents) and mentions the hard limit constraint ('max 1000'). Missing operational details like rate limits, authentication requirements, or error handling behaviors.

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?

Well-structured with clear separation between conceptual explanation and Args documentation. Front-loaded with purpose and distinction logic. Minor redundancy between opening sentence and Args crime_category description. The 'Args:' section efficiently maps parameters to semantics without excessive prose.

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?

Appropriately complete given the output schema exists (no need to describe return values). The critical NIBRS data model concept (offense vs incident) is explained, parameter constraints are documented, and sibling differentiation is addressed through the granularity explanation. Sufficient for a filtered retrieval tool of this complexity.

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 has 0% description coverage (only titles), so description fully compensates by documenting all 4 parameters in the Args section. crime_category and limit are well-specified with defaults and constraints; indicator is minimally described ('Filter by offense type'); domain is vague ('Filter by analysis domain'). Good compensation for the schema deficiency.

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?

Excellent specificity: 'Get NIBRS offense-level estimates' provides verb (Get), resource (NIBRS offense-level estimates), and scope (person or property crimes). Crucially, it distinguishes from sibling incident tools by explicitly contrasting 'offense-level data (as opposed to incident-level)' and explaining that 'offense counts are typically higher than incident counts.'

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?

Provides implicit guidance by explaining the offense vs. incident distinction (multiple offenses per incident), which helps users understand when to select this over incident-level siblings. However, it fails to explicitly name the alternative tools (e.g., 'use get_violent_incident_estimates for incident-level data') or provide explicit when-not-to-use conditions.

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 provided, so description carries full burden. Adds valuable domain context ('weighted national estimates', specific crime types, breakdown dimensions) but omits behavioral specifics like pagination logic, rate limits, or data freshness despite having limit parameters suggesting pagination.

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?

Well-structured with clear separation between purpose statement, return value description, and structured Args documentation. No filler text; every sentence provides specific domain or usage context.

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 3 optional parameters and existing output schema, description adequately covers tool purpose and parameter semantics. Could improve by noting behavior when no filters applied or explaining relationship between 'indicator' and 'domain' parameters.

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?

Excellent compensation for 0% schema description coverage. Args section documents all 3 parameters with semantic meaning ('Filter by offense type', 'Filter by analysis domain') and concrete examples for each, exceeding what the 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?

Specific verb 'Get' + resource 'national estimates of property crime incidents from NIBRS'. Clearly distinguishes from sibling 'get_violent_incident_estimates' by specifying property crimes (burglary, larceny, etc.) vs violent 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?

Implies usage through domain-specific language (distinguishes property crimes from violent crimes), but lacks explicit guidance on when to use vs 'get_offense_estimates' or whether this is a subset of that data.

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 the full burden. It adequately discloses what data is returned (victim-level demographics, relationships, injury types) and mentions counts/percentages, but omits behavioral details like rate limits, authentication requirements, or pagination behavior beyond the limit parameter.

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 well-structured with a clear opening statement of purpose, followed by return value details, use case, and an Args section. Every sentence adds value without excessive verbosity, though the Args section repeats information that ideally would be in the schema.

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 3 parameters are well-documented in the description and the return values are explained (filling the role of the unseen output schema), the description is minimally complete. However, gaps remain regarding explicit differentiation from similarly-named sibling tools and behavioral constraints.

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% description coverage, requiring the description to fully compensate. It successfully provides clear semantic meaning for all three parameters with concrete examples (e.g., 'Aggravated Assault' for indicator, 'Victim-offender relationship' for domain), effectively documenting the parameter purposes.

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 identifies the tool retrieves 'NIBRS victimization counts and percentages' and specifies it returns 'victim-level data' including demographics and relationships. It implicitly distinguishes from siblings (which focus on offense/property/incident estimates) by emphasizing victim-centric data, though it doesn't explicitly name the siblings.

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 implied usage context ('Useful for understanding who is affected by crime'), suggesting demographic analysis use cases. However, it lacks explicit guidance on when to choose this tool over sibling estimation tools like 'get_violent_incident_estimates'.

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 provided, so description carries full burden. Adds valuable context about NIBRS data source, 'weighted' nature of estimates, and available breakdowns. However, lacks disclosure on authentication needs, rate limits, pagination behavior beyond 'limit', or data freshness.

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?

Well-structured with purpose statement upfront, followed by return value description, then Args section. Appropriate length given need to document parameters. Minus one point only because the density of the enumerated domain options slightly disrupts flow, though this is necessary 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?

Comprehensive given presence of output schema (excusing return value description). Covers data source, filtering capabilities, and pagination. Minor gap: no mention of date range filtering or temporal constraints which are typical for crime data APIs.

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?

Excellent compensation for 0% schema description coverage. Args section provides semantic meaning for all 3 parameters: 'indicator' includes examples like 'Aggravated Assault', 'domain' lists valid options like 'Weapon involved', and 'limit' clarifies default/max values. Critical given schema objects only contain titles/types.

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?

Clear verb ('Get'), specific resource ('national estimates of violent crime incidents from NIBRS'), and scope ('crimes against persons'). Distinguishes from sibling get_property_incident_estimates by explicitly listing violent crime types (aggravated assault, homicide, etc.) and noting 'crimes against persons'.

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?

Implicitly distinguishes usage by detailing violent crime categories and analysis domains (weapon, location type), but lacks explicit when-to-use guidance vs siblings like get_offense_estimates or get_victimization_data. No mention of prerequisites or exclusions.

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