@pipeworx/us-dmv

@pipeworx/us-dmv

Access US state DMV data including vehicle registrations, EV adoption, DMV office locations and services, live wait times, and California forms and insurer lookups. Supports multiple states with per-state quirks documented.

Category
访问服务器

README

@pipeworx/us-dmv

US state DMV data: vehicle and electric-vehicle registrations, DMV office locators with per-office services, live field-office wait times, and California form and insurer lookups. Keyless.

Why coverage is state-by-state

This pack came out of a survey of all 50 states plus DC (2026-07-29). Roughly 30 states publish nothing machine-readable about motor vehicles — annual PDFs at best. So each tool takes a state argument backed by a short, explicit list, and an unsupported state returns { found: false, reason: 'state_unsupported', hint, supported_states } rather than an empty result that reads like a data bug.

Grain differs per state and is not normalisable away, so every response reports grain, as_of, source, and — where a row list can be cut off — truncated.

Tools

Tool States What it returns
dmv_vehicle_registrations CA, DE, MD, NY, PA, TX, WA Registered vehicle counts by county, ZIP, fuel, make, model year or class
dmv_ev_adoption CA, CT, DE, HI, MD, NY, PA, WA Battery-electric and plug-in hybrid counts, plus EV share where a denominator exists
dmv_wait_times NC, OR Live average/max wait, customers queued, open-or-closed per field office
dmv_offices CA, DC, IA, IN, MO, NC, NJ, NY, TN, WI Addresses, hours, coordinates, and which services each office performs
dmv_ca_forms CA Official DMV form name, REG/DL number and download page
dmv_ca_insurance_lookup CA Insurer NAIC codes by company name

Auth

None. Every upstream is a public state endpoint. _apiKey is accepted on the Socrata-backed tools and passed as X-App-Token; the public rate limits are ample without it.

Per-state gotchas worth knowing

These are all live-verified behaviours, not guesses:

  • WA registrations count transactions, not fleet size. hmzg-s6q4 publishes registration transactions per month (King County runs ~160k/month), so it cannot answer "how many vehicles are registered here". The tool scopes to one fiscal year or month and says so in grain. For a Washington fleet count of plug-in vehicles use dmv_ev_adoption, which reads the VIN-level EV population file instead.
  • NY mixes vehicle types. w4pv-hbkt holds trailers, boats and snowmobiles alongside road vehicles; the tool defaults to record_type=VEH. County names are unabbreviated, so Brooklyn is KINGS.
  • CA snapshots are one resource per year, 2019–2026, and the rows carry the prior December 31 as their as-of date. Grouping by ZIP surfaces a literal OOS bucket (out-of-state registrants) that outranks every real ZIP — filter it out for a ZIP ranking.
  • CA "OTHER/UNK" make absorbs small-count suppression, so it can outrank real makes.
  • MD spells one county two ways: PRINCE GEORGE'S in the registration dataset, PRINCE GEORGES in the EV dataset. MD also reports the registrant's county of residence, so a few out-of-state counties appear with small counts.
  • PA quarters are columns, not rows (TOTAL_EV_Q1..Q4); the tool picks the latest quarter that actually carries data. TOTAL_EV is BEV + PHEV — conventional hybrids sit in HEV.
  • NC and OR waits read zero and closed outside business hours. That is the agency feed reporting an idle queue, not a missing value, so the response flags it in note rather than erroring. NC's feed is a unit tree (root, districts, then offices); the pack keeps leaf units.
  • OR has no JSON endpoint — the office array is parsed out of the server-rendered page. Upstream also ships per-office ipAddress and machine fields, which are dropped here.
  • TX was last refreshed in 2023 and is statewide-annual only. CT was last refreshed in February 2025. Both report this in note rather than pretending to be current.
  • NJ's dmv_offices entries are inspection and emission-repair facilities, not licensing offices, and the upstream county column disagrees with city on some rows.
  • DC coordinates need reprojection — the layer stores State Plane, so the pack requests outSR=4326 geometry rather than reading XCOORD/YCOORD.
  • CA form search is the website's own relevance ranking, so a query like "title transfer" can put a vessel-title form first.
  • Maryland's WAF is picky about SoQL query shape. opendata.maryland.gov answers both a wildcard like '%x%' clause and a two-condition where with a Cloudflare interstitial (403 "Just a moment...") instead of data — reproducibly, and only on that domain: NY, WA and CT accept the identical clauses. It only reproduces from a deployed Worker, never from a laptop, so local testing will not catch it. All four Maryland paths therefore send the one shape that gets through — a single year_month condition — and narrow to a county or ZIP in code, which is cheap because one month is a few hundred rows.
  • WisDOT's ArcGIS server is intermittently slow — it overran a 12s budget on the first production call and answered in ~1s on retry. Every tool retries once and then returns { error, hint } telling the caller to retry, rather than reporting an upstream stall as missing data.

Data sources

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "us-dmv": {
      "url": "https://gateway.pipeworx.io/us-dmv/mcp"
    }
  }
}

Or connect to the full Pipeworx gateway for access to all 1370+ data sources:

{
  "mcpServers": {
    "pipeworx": {
      "url": "https://gateway.pipeworx.io/mcp"
    }
  }
}

Using with ask_pipeworx

Instead of calling tools directly, you can ask questions in plain English:

ask_pipeworx({ question: "your question about US State DMV data" })

The gateway picks the right tool and fills the arguments automatically.

More

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选