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cyntrica

Gov Data MCP

by cyntrica

fbi_nibrs

Read-only

Get FBI NIBRS crime data with victim and offender details, relationships, weapons, location, and time for 71 offenses. Use offense codes for precise queries.

Instructions

Get NIBRS (National Incident-Based Reporting System) data from the FBI. More detailed than summarized UCR data — includes victim/offender demographics, relationships, weapons, location, and time of day for 71 offense types. Offense codes use NIBRS format: '13A' (aggravated assault), '09A' (murder), '23H' (all other larceny), '35A' (drug violations), '220' (burglary), etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
oriNoAgency ORI code for agency-level data
typeNoData type (default: counts)
stateNoTwo-letter state abbreviation for state-level data
offenseYesNIBRS offense code: '100' (Kidnapping/Abduction), '101' (Treason), '103' (Espionage), '120' (Robbery), '200' (Arson), '210' (Extortion/Blackmail), ... (72 total)
to_yearNoEnd year
from_yearNoStart year
Behavior3/5

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

The readOnlyHint annotation already covers the read-only nature, so the description does not need to restate it. It adds context about the data richness and offense code format, but does not disclose other behavioral traits such as pagination, rate limits, or return structure. With the annotation providing the safety profile, this is adequate but not extensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the tool's identity and resource, and packs substantive detail without fluff. It is well-structured and appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of an output schema, the description provides a strong overview of the data type and offense code format, which helps the agent understand what to expect. It does not explain return structure or the type parameter, but the schema covers parameter semantics. Overall, it is fairly complete for a data retrieval tool with a rich parameter schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all parameters are documented in the schema. The description adds value for the offense parameter by giving real-world examples (13A, 09A, etc.) and confirming the NIBRS code format. However, it introduces a minor inconsistency by saying 71 offense types while the schema lists 72, and it does not add information about other parameters beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves NIBRS data from the FBI with a specific verb (Get) and resource. It explicitly contrasts with summarized UCR data, distinguishing it from sibling FBI crime tools like fbi_crime_summarized, and provides concrete offense code examples, making its purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool—when detailed incident-level data (demographics, weapons, location, time of day) is needed rather than summarized UCR. It contrasts with summarized UCR data, indicating a clear use case, but it does not name a specific alternative tool or state explicit exclusion criteria.

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

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