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Lavoiedavidw

Charlotte-Mecklenburg MCP Server

by Lavoiedavidw

violent_crime_near

Query recent violent crime offenses near a point by address or lat/lon, with radius and time window, returning total counts and breakdown by offense type.

Instructions

CMPD violent-crime offense counts (Homicide/Rape/Robbery/Assault/...) near a point.

Provide either address (free-form string) or both lat and lon. Same shape/limitation as crime_near: ViolentCrimeData is a monthly-aggregate table keyed by Neighborhood Profile Area, not incident-level points, so nearby NPAs are resolved via 311 density (see module docstring) and OFFENSE_COUNT is summed by OFFENSE_DESCRIPTION for months within days of now.

Args: address: Free-form address, e.g. "600 E 4th St, Charlotte, NC". lat: Latitude, used with lon if address is not given. lon: Longitude, used with lat if address is not given. radius_m: Radius in meters used to resolve nearby NPAs (default 800). days: Only include months within this many days of now (default 180). limit: Maximum number of raw NPA/month/offense records to return (default 50); the breakdown totals cover all matching months.

Returns: Dict with nearby_npas, records (capped at limit), breakdown (offense description -> total count), total_offenses, radius_m, days, lat, lon, resolved_address, and a summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNo
lonNo
daysNo
limitNo
addressNo
radius_mNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It thoroughly explains the aggregated nature of the data, the resolution mechanism via 311 density, the summation logic, and the output structure. This gives users a clear understanding of the tool's limitations and behavior.

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 well-structured with a clear opening summary, an Args section, and a Returns section. It is front-loaded with the primary purpose. Each sentence adds substantive detail, and the formatting makes it easy to scan. No wasteful repetition.

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

Completeness5/5

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

The description covers all essential aspects: the tool's purpose, input modes, data caveats, parameter roles, and return keys. Despite the existence of an output schema, the description adds valuable context about data aggregation and resolution that a schema cannot convey. It is sufficiently complete for an agent to select and invoke the tool correctly.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully compensate. It does so by providing detailed explanations for every parameter: address (with example), lat/lon (with conditional usage), radius_m, days, and limit (including its effect on records vs breakdown). This exceeds the schema's minimal property names and defaults.

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's function: 'CMPD violent-crime offense counts (Homicide/Rape/Robbery/Assault/...) near a point.' This specifies the verb (counts), resource (violent-crime offenses), and scope (near a point), effectively distinguishing it from siblings like crime_near and homicides_near.

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 provides clear usage context, explaining the input modes ('Provide either address or both lat and lon') and the data limitations. It references the sibling tool crime_near, but does not explicitly state when to use this tool versus alternatives such as crime_near or homicides_near, so it stops short of a perfect score.

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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