CIS Automotive API MCP Server
The CIS Automotive API, provided by Auto Dealer Data, is a comprehensive RESTful service designed to grant programmatic access to a vast repository of automotive market intelligence.
Quick Start Summary
The CIS Automotive API MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the CIS Automotive API API through natural language. It exposes 10 API endpoints as callable tools, such as Days worth of supply left on dealer lots, Days a vehicle takes to sell, Get a list of brand names, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/autodealerdata-com. This integration is sourced from the auto CIS Automotive API OpenAPI specification (v1.0) and has a quality score of 34/99 (fair documentation coverage).
Server Details
- Category
- Security
- Authentication
- None
- Endpoints
- 10 operations
- Transport
- STDIO
- Spec Version
- v1.0
- Install Command
npx -y @mcp/autodealerdata-com
Environment Variables
CIS_AUTOMOTIVE_API_API_KEYExample: your_cis_automotive_api_api_key
Top Endpoints
/daysSupplyDays worth of supply left on dealer lots
/daysToSellDays a vehicle takes to sell
/getBrandsGet a list of brand names
/getDealersPremium. Dealers in a zip code.
/getDealersByIDPremium. Dealers by ID
One-Click Install
Copy the snippet for your MCP client and paste it in — zero editing required.
Claude Desktop
Add to claude_desktop_config.json
{
"mcpServers": {
"autodealerdata-com": {
"command": "npx",
"args": [
"-y",
"@mcp/autodealerdata-com"
],
"env": {
"CIS_AUTOMOTIVE_API_API_KEY": "your_cis_automotive_api_api_key"
}
}
}
}Cursor
Settings → MCP Servers → Add
{
"mcpServers": {
"autodealerdata-com": {
"url": "https://mcpbridge.org/config/autodealerdata-com.json"
}
}
}Saves as .cursor/mcp.json in the download. Move it to your project root.
VS Code
Use with MCP extension
{
"mcpServers": {
"autodealerdata-com": {
"url": "https://mcpbridge.org/config/autodealerdata-com.json"
}
}
}Endpoints Explorer
Search and browse the 10 operations supported by this server.
Multi-Language Code Examples
Executable code snippets for calling CIS Automotive API endpoints in curl, TypeScript, or Python.
curl -X GET "https://api.apis.guru/v2/specs/autodealerdata.com/1.0/daysSupply" \ -H "Content-Type: application/json" \ # No auth required
Manual Configuration
Directly add this block to your JSON config file, or use the hosted config registry URL.
{
"mcpServers": {
"autodealerdata-com": {
"command": "npx",
"args": ["-y","@mcp/autodealerdata-com"],
"env": {
"CIS_AUTOMOTIVE_API_API_KEY": "your_cis_automotive_api_api_key"
}
}
}
}Authentication Details
No authentication required. This MCP server runs out-of-the-box.
Documentation Links
Error Handling & HTTP Status Code Matrix
Common status codes, error causes, and resolution steps when invoking CIS Automotive API endpoints.
400 Bad RequestCause: Malformed payload parameters or missing required fields.
Resolution: Verify request schema in Endpoints tab before calling tool.
401 UnauthorizedCause: Missing or invalid API key credentials.
Resolution: Set environment variable in MCP client config under env object.
403 ForbiddenCause: Insufficient scope permissions for requested resource.
Resolution: Verify key permissions in developer dashboard.
404 Not FoundCause: Resource URL path or requested entity ID does not exist.
Resolution: Check path variables and parameters.
429 Rate Limit ExceededCause: API rate limit quota exceeded.
Resolution: Implement exponential backoff retry in tool call.
500 Internal ErrorCause: Upstream server runtime fault.
Resolution: Inspect STDIO stderr output for log trace.
Quality Score
Checked against our protocol-compliance rules.
Specification Version: v1.0
Page compiled on: June 13, 2026
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📖 Detailed MCP Integration Guide
A technical breakdown of capabilities, agent workflows, and security/configuration best practices.
/getDealersByRegion and /getDaysToSell endpoints to fetch the relevant dataset and then generate the React or Vue code to visualize it accurately, eliminating manual data research and API integration boilerplate./getRegionMarketShare to understand the competitive landscape, /getBrands to resolve brand names, and /daysSupply for the specific comparative metrics, synthesizing the results into a coherent analysis. Another workflow could be: "Write a Python script that uses this API to monitor for new dealers added to the system and alerts us via email." The AI could use /getDealers as a baseline, implement a polling mechanism, and generate the necessary logic for difference detection and notification integration.Similar APIs
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