Vehicle Imagery MCP Server

Vehicle Imagery MCP Server

A Model Context Protocol server for the Vehicle Imagery API that lets AI assistants browse vehicle catalogs and resolve studio quality car images with customizable options like paint colors, camera views, shadows, and transparent backgrounds.

Category
访问服务器

README

Vehicle Imagery MCP Server

npm version license

A Model Context Protocol server for the Vehicle Imagery API. It lets AI assistants such as Claude browse the vehicle catalog and resolve studio quality car images as signed CDN URLs, with paint colors, camera views, shadows, transparent backgrounds and more.

Built on the official vehicleimagery SDK.

What it enables

Ask your assistant things like:

  • Show me a 2023 BMW X5 from the front in Alpine White
  • Which colors are available for the Tesla Model 3
  • Get me a transparent rear view of an Audi Q5 as WebP, 1200 px wide

The assistant walks the catalog, resolves a signed image URL and can embed it directly.

Requirements

  • Node.js 18 or newer
  • A Vehicle Imagery API key

Install

Add to Cursor Add to VS Code Add to Claude Code Add to Claude Desktop Add to Codex Add to Antigravity Add to Windsurf Add to Gemini CLI

The Cursor and VS Code buttons install directly. The other tools have no install link standard, so their buttons jump to the matching setup section below. Replace YOUR_API_KEY with your key after installing.

Setup

Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "vehicleimagery": {
      "command": "npx",
      "args": ["-y", "vehicleimagery-mcp"],
      "env": {
        "VI_API_KEY": "your_api_key"
      }
    }
  }
}

Claude Code

claude mcp add vehicleimagery --env VI_API_KEY=your_api_key -- npx -y vehicleimagery-mcp

Cursor

Use the install button above, or add this to .cursor/mcp.json in your project (or ~/.cursor/mcp.json globally):

{
  "mcpServers": {
    "vehicleimagery": {
      "command": "npx",
      "args": ["-y", "vehicleimagery-mcp"],
      "env": {
        "VI_API_KEY": "your_api_key"
      }
    }
  }
}

VS Code

Use the install button above, or add this to .vscode/mcp.json in your workspace:

{
  "servers": {
    "vehicleimagery": {
      "command": "npx",
      "args": ["-y", "vehicleimagery-mcp"],
      "env": {
        "VI_API_KEY": "your_api_key"
      }
    }
  }
}

OpenAI Codex

codex mcp add vehicleimagery --env VI_API_KEY=your_api_key -- npx -y vehicleimagery-mcp

Or add this to ~/.codex/config.toml:

[mcp_servers.vehicleimagery]
command = "npx"
args = ["-y", "vehicleimagery-mcp"]
env = { "VI_API_KEY" = "your_api_key" }

Google Antigravity

Open the MCP server settings in the agent panel, choose to edit the raw config and add:

{
  "mcpServers": {
    "vehicleimagery": {
      "command": "npx",
      "args": ["-y", "vehicleimagery-mcp"],
      "env": {
        "VI_API_KEY": "your_api_key"
      }
    }
  }
}

Windsurf

Add the same mcpServers block as above to ~/.codeium/windsurf/mcp_config.json.

Gemini CLI

Add the same mcpServers block as above to ~/.gemini/settings.json.

Any other MCP client

The server speaks the standard stdio transport. Launch command: npx -y vehicleimagery-mcp with the VI_API_KEY environment variable set. Alternatively pass the key as an argument: npx -y vehicleimagery-mcp --api-key your_api_key.

Tools

Tool Purpose
search_vehicles Fuzzy search brands and models by free text
list_brands List every available brand
list_models List the models of a brand
list_years List the model years of a brand and model
list_variants List the variants of a brand, model and year
list_trims List the trims of a configuration
get_vehicle_config Views, paint colors and features of one configuration in one call
get_vehicle_image Resolve a signed image URL with transform options
list_reference_views All camera view names known to the service
list_reference_colors All paint color names known to the service
get_account_info What the configured API key is allowed to do

The catalog is a hierarchy: brand, model, year, variant, trim, view. The tool descriptions guide the assistant through it automatically.

Image options

get_vehicle_image supports: format (png, webp, jpeg, avif, auto), resolution presets, ratio padding, explicit width, height and quality, paint color by name, shadow, transparency, ground and mirroring. The returned image_url is a signed CDN URL that stays valid for about 7 days and can be embedded directly.

Environment variables

Variable Purpose
VI_API_KEY Your Vehicle Imagery API key (required)
VI_BASE_URL Override the API base URL (optional)

Development

git clone https://github.com/vehicleimagery/vehicleimagery-mcp.git
cd vehicleimagery-mcp
npm install
npm run typecheck
npm test
npm run build

Run the built server locally:

VI_API_KEY=your_key node dist/cli.js

You can also use the server programmatically:

import { createServer } from "vehicleimagery-mcp";
import { VehicleImagery } from "vehicleimagery";

const server = createServer(new VehicleImagery({ apiKey: "your_key" }));

Related

License

MIT, Vehicle Imagery.

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