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lzinga

US Government Open Data MCP

by lzinga

fbi_use_of_force

Read-onlyIdempotent

Retrieve FBI use-of-force data for federal or national law enforcement incidents involving death, serious injury, or firearm discharge. Query by year and scope.

Instructions

Get Use of Force data from the FBI. Covers incidents where law enforcement use of force resulted in death, serious injury, or firearm discharge. Available at federal (all federal agencies) or national (all participating agencies) level. Use scope='federal' for federal agencies, 'national' for all agencies participation data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesYear to query (2019-present)
scopeYes'federal' = federal UoF by year, 'national' = national UoF participation by year
quarterNoQuarter (default: 4 = full year cumulative)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2026.4.11
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv2026.3.9

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is fully covered. The description adds useful context about the incidents covered and the federal/national levels, but it does not disclose additional behavioral details such as response shape, pagination, or update cadence.

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?

Three concise sentences lead with the core action, then define the data coverage, then give concrete parameter guidance. Every sentence earns its place, and the scope instruction is front-loaded before the parameter mapping.

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?

For a read-only, idempotent query tool with 100% schema coverage and only three simple parameters, the description covers purpose, data scope, and scope selection sufficiently. It could mention the quarter default behavior or output format, but the schema already documents quarter and annotations carry the safety context.

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 year, scope, and quarter are already documented in structured form. The description adds natural-language clarity to the scope enum with 'all federal agencies' and 'all participating agencies,' but it does not meaningfully explain year or quarter 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 opens with a specific action and resource ('Get Use of Force data from the FBI') and clarifies the exact incident types covered. It also disambiguates the two scopes ('federal' vs 'national'), making it easy to distinguish this tool from other FBI siblings like fbi_crime_summarized or fbi_arrest_data.

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 gives explicit direction for choosing scope: 'Use scope=\'federal\' for federal agencies, \'national\' for all agencies participation data.' It does not explicitly name alternative tools or say when not to use this tool, but the clear data coverage makes the appropriate context obvious.

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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