Skip to main content
Glama

Traffic crashes near a location

get_traffic_crashes_near
Read-onlyIdempotent

Check reported traffic crashes near any Mecklenburg County address or coordinates, with counts by severity, to evaluate intersection safety or support traffic-calming requests.

Instructions

Reported traffic crashes near a location, with counts by severity.

Useful for "is this intersection dangerous?" or traffic-calming requests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoHow many days back to look.
limitNoMaximum number of records to return.
locationYesA street address in Mecklenburg County, e.g. '600 E 4th St, Charlotte, NC 28202', or coordinates as 'latitude,longitude', e.g. '35.2271,-80.8431'.
radius_milesNoRadius in miles.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNo
sinceNoEarliest date included (local time).
recordsYes
locationNo
breakdownNoCounts of all matching records per value.
breakdown_byNo
radius_milesNo
total_matchingYesRecords matching the filters; may exceed returned.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare this is a read-only, idempotent, non-destructive open-world query, so the safety profile is covered. The description adds that results include counts grouped by severity, but says nothing about data recency, rate limits, coverage gaps, or what happens when no crashes match.

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?

Two short sentences with zero filler; the core capability and the motivating use cases are front-loaded in that order. Nothing redundant with the title or schema.

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?

An output schema exists, so return-value detail is unnecessary, and the description supplies purpose plus task context for a four-parameter spatial query. It could do slightly more to orient the agent among the many '*_near' siblings, but it is sufficient to call the tool correctly.

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% and each of the four parameters (days, limit, location, radius_miles) is documented in the schema, including the address/coordinate format for location. The description adds no parameter-level meaning beyond that, so the baseline of 3 applies.

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

Purpose4/5

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

States a specific verb ('get'), resource ('traffic crashes'), and spatial scope ('near a location'), plus the output shape ('counts by severity'). It is clearly distinguishable from siblings like get_crime_near or get_311_requests_near by resource, though it never names an alternative explicitly.

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?

Provides concrete usage context with the example questions 'is this intersection dangerous?' and 'traffic-calming requests', which tell the agent what kind of tasks this tool serves. It stops short of stating when NOT to use it or pointing to sibling tools for adjacent datasets.

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