CarAPI MCP for AI. Query 66,000+ vehicle specs by criteria.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
CarAPI MCP connects your agent to a database of 66,000+ vehicles. Find cars by year, make, model, or trim; filter down options using specs like engine type, body style, and transmission; retrieve full details including MSRP and fuel economy data.
What your AI can do
Get bodies
Returns a list of all recognized car body types, such as sedan or SUV.
Get drives
Provides an exhaustive list of available drivetrain types (e.g., FWD, AWD).
Get engines
Outputs a complete catalog of all engine configurations and capacities.
Retrieves lists of body types, engine kinds, drive systems, and transmissions used in the database.
Gets a list of every car make; optionally filters this list by a specific year.
Retrieves available models for a given manufacturer, with the ability to limit results to a certain year.
Searches through the main database using multiple criteria like year, make, model, body type, engine specs, or transmission.
Pulls full specifications for a single car, including MSRP, fuel economy numbers, and dimensions.
Ask an AI about this
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CarAPI: Your Automotive Data Toolkit (8 Tools)
These tools let your agent structure complex queries; use them to define years, makes, bodies, engines, and other parameters before running a vehicle search.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using CarAPI on VinkiusGet Bodies
Returns a list of all recognized car body types, such as sedan or SUV.
Get Drives
Provides an exhaustive list of available drivetrain types (e.g., FWD, AWD).
Get Engines
Outputs a complete catalog of all engine configurations and capacities.
Get Makes
Returns the names and IDs of car manufacturers, allowing filtering by year.
Get Models
Retrieves all model names for a specific make, limited to a given year.
Get Transmissions
Lists every type of transmission available in the database.
Get Vehicles
Performs the main search, returning full details on vehicles based on multiple criteria like make, model, and year.
Get Years
Provides a list of all years available in the dataset for filtering searches.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with CarAPI, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CarAPI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 8 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Finding specific vehicle data used to be a pain in the butt.
Today, getting reliable specs means clicking through multiple manufacturer websites. You start on one site for engine details; you jump to another for MSRP and then a third just to check body types. You spend hours copy-pasting data from various tabs into an Excel sheet, hoping the numbers match up.
With this MCP, your agent handles all those manual steps. You ask a single question—for instance, 'Find me three SUVs under $40k.' The system runs multiple checks using `get_bodies` and `get_vehicles`, providing you with a clean list of results in one shot; the data is ready to use.
The CarAPI MCP gives you structured vehicle data.
You no longer have to guess what filtering options are available. Instead of scrolling through poorly organized menus, you can programmatically call `get_engines` or `get_drives` to get a clean list of all valid parameters for your application's dropdown menus.
This means your app doesn't break when the data changes; it adapts because it references the authoritative lists provided by this MCP. The system just works.
What your AI can actually do with this
This connector lets you talk to an entire vehicle database, getting specific car facts through natural conversation. You don't need to know the exact table structure or which query parameters are needed; your AI client handles that complexity for you. For instance, instead of running multiple queries to narrow down options, you simply ask, 'Show me electric SUVs from 2024 with less than $50k.' The system then finds those cars and gives you their full specs, including engine capacity, transmission type, and MSRP.
It's a massive resource for anyone building anything that deals with auto data. If you're looking to manage inventory or compare models, connecting this MCP through the Vinkius catalog keeps everything in one place, so your agent can access all automotive data without needing separate integrations.
019d8422-e968-720c-b585-e34fe6cff02f Here's how it actually works
The bottom line is your agent handles the complex data querying; you just ask what car you're looking for.
Subscribe to the CarAPI MCP on Vinkius and provide your API key.
Your AI client uses natural language prompts (e.g., 'Find all diesel pickups from 2018').
The MCP translates that request into structured calls across multiple tools, returning a clean list of vehicles and their specs.
Who is this actually for?
Anyone who needs to work with structured vehicle data. This isn't for casual lookups; it's for systems that need reliable, consistent specs on thousands of models.
Uses the MCP to cross-reference market data against specific model years, pulling details like engine types and fuel economy for reports.
Runs bulk searches using get_vehicles to verify specs or find comparable models when updating inventory records.
Integrates the data into comparison widgets, relying on tools like get_makes and get_models to structure their application's UI.
What Changes When You Connect
You can compare cars based on specific mechanics; instead of manual lookups, your agent uses get_engines and filters results to only show vehicles with the required engine type.
When you need a starting list, you don't have to guess. Use get_years, get_makes, or get_models first to define parameters before running a full search; it prevents bad queries.
The system doesn't just give you names. The main function is pulling everything—MSRP, dimensions, and fuel economy—in one go using the get_vehicles tool for comparison groups.
You can structure your app logic perfectly by first calling get_bodies to get a list of acceptable body styles, then passing that list into your primary search query.
It handles all the structural data points for you. For example, if you need to know what transmissions are available, you just call get_transmissions without needing any other input.
See it in action
A dealer needs instant inventory checks.
The manager asks the agent: 'Show me all 2019 SUVs that use a V6 engine and are FWD.' The agent runs get_vehicles using all those filters, providing an immediate list of available VINs and specs.
A developer builds a comparison tool.
The developer uses the MCP to first call get_makes, then loops through each make calling get_models to build a full, searchable dropdown menu for their website.
An analyst tracks market trends.
The analyst asks: 'What was the average MSRP range for sedans across all major makes between 2015 and 2020?' The agent pulls data using get_bodies to filter by sedan, then runs get_vehicles over the specified decade.
A user wants basic model options.
The user simply asks: 'What models does Toyota make?' The agent uses get_makes followed by get_models, returning a concise list of available vehicles and their general year range.
The honest tradeoffs
Searching without defining the scope
Just asking, 'Tell me about cars.' The agent has no filters; it can't pull meaningful data because it doesn't know if you mean a truck or an SUV.
Always define your search parameters first. Start by calling get_years to narrow the timeframe, then use get_makes and finally pass that into get_vehicles for accurate results.
Assuming all data is in one place
Trying to build a custom database schema from memory. You'll miss crucial details like which body type corresponds with a specific drive system.
Use the specialized tools, such as get_bodies and get_drives, to query the reference data first; this builds your knowledge base before you run the main vehicle search.
When It Fits, When It Doesn't
You use this MCP if your core problem involves structured comparison or bulk data retrieval across thousands of unique products, like cars. If you need that level of detail—MSRP, fuel economy, specific engine specs—this is necessary. Don't use it if you just want a general list; for instance, if you only need to know 'What are the top 5 car manufacturers?' then calling get_makes directly is simpler than running through the full search flow. You must remember that this is a structured database connection, not a general web scraper; always use its specialized tools like get_bodies and get_transmissions to validate your parameters first.
Questions you might have
How do I get a CarAPI key? +
Sign up at carapi.app and get your API key from the dashboard. Free tier available with limited requests.
How many vehicles are in the database? +
The database includes 66,000+ vehicles from 1990 to current year, covering all major manufacturers and models sold in the US market.
Can I filter vehicles by engine type? +
Yes! Use get_vehicles with engine_type parameter to filter by gas, diesel, electric, hybrid and more.
If I need to know all available car body types before searching, what tool should I use? (get_bodies) +
You must call get_bodies first. This returns a complete list of recognized body styles like 'Sedan' or 'SUV'. Using this reference data prevents failed searches when you run get_vehicles later.
What fields are included in the output when I use the get_vehicles tool? (get_vehicles) +
The results provide deep specs, including MSRP, fuel economy details, engine size, transmission type, and body style. This comprehensive data set lets your agent pull everything you need into a single record.
How does the MCP handle invalid inputs or missing vehicle criteria? (get_vehicles) +
The system validates all parameters before running the search. If an input is out of scope, it returns a specific error code and clear message detailing which field needs fixing. You don't get vague failures.
Are there any rate limits or usage restrictions when I query this MCP? (get_vehicles) +
Usage is governed by your Vinkius subscription tier, but we track activity to ensure stability. High-volume querying will trigger throttling messages, telling you exactly how long to wait before trying again.
Can I use the get_years tool to determine what years are available for filtering? (get_years) +
Yes, calling get_years immediately gives you a definitive list of all supported model years. This is crucial because it confirms the date range before you even attempt to call get_makes or get_models.
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