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city-data-mcp

Official

Query Traffic Safety & Congestion

query_traffic

Fetch traffic safety and congestion data for any US city by name, including fatal crash stats and pedestrian, cyclist, and alcohol-related breakdowns.

Instructions

Traffic safety data from NHTSA FARS and TTI congestion metrics. US only. Returns fatal crash statistics (2019-2022) including pedestrian, cyclist, and alcohol-related breakdowns.

County-level data as primary view with state-level context. Congestion data for 33 metros.

No API key needed. Works for any US city via geo-resolver.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYesCity name (e.g., 'Denver', 'Austin', 'NYC')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does usefully disclose data vintage (2019-2022), coverage limits (county primary view, congestion only for 33 metros), and that no API key is required. However it omits response behavior, error handling, and anything about result size or freshness beyond the fixed year range.

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

Conciseness4/5

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

Front-loaded with the data sources and scope, then coverage caveats. Every sentence carries information, though the staging of 'No API key needed' and the geo-resolver line is slightly disjointed from the rest.

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?

With no output schema, the description steps in to describe return contents (fatal crash statistics with pedestrian, cyclist, and alcohol breakdowns) and the county/state granularity. That is enough to call it correctly, though it doesn't hint at response shape or volume.

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?

There is a single parameter with 100% schema description coverage and inline examples ('Denver', 'Austin', 'NYC'). The description adds no format or naming guidance beyond the geo-resolver claim, so the schema does the work and a baseline 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?

The description names a specific topic (traffic safety + congestion) and its underlying datasets (NHTSA FARS, TTI), which clearly distinguishes it from siblings like query_transit or query_city_data. It never explicitly contrasts itself with those alternatives, but the resource is unambiguous.

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

Usage Guidelines2/5

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

It states the geographic constraint ('US only') and that any US city works via the geo-resolver, but there is no when-to-use guidance, no mention of when to prefer query_transit or query_city_data, and no exclusions. The agent must infer the use case entirely.

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