CarAPI MCP. Get specs and pricing for any vehicle, instantly.
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CarAPI. Search over 66,000 vehicles using natural language. Filter cars by year, make, model, body type, engine specs, transmission, and pricing data.
Get full specs—including MSRP, fuel economy, and dimensions—for virtually any vehicle from 1990 to today.
What your AI agents can do
Get bodies
Returns a list of all body types (e.g., Sedan, Truck, SUV).
Get drives
Returns a list of all drive types (e.g., FWD, AWD, RWD).
Get engines
Returns a list of all engine types (e.g., V6, Turbo, Electric).
The agent searches the database and returns vehicle records based on filters like year, make, model, and body type.
The agent fetches complete lists of standard automotive attributes, such as all available body types, engine types, or transmissions.
The agent lists all car manufacturers or gets the specific models available for a selected make and year.
The agent gets detailed data on a specific vehicle, including engine size, fuel economy, and the Manufacturer's Suggested Retail Price (MSRP).
The agent returns a list of years for which vehicle data exists in the database.
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Supported MCP Clients
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CarAPI MCP Server: 8 Tools for Automotive Data
Use these tools to look up everything from car makes and models to full vehicle specs, engines, and body types.
019d8422get bodies
Returns a list of all body types (e.g., Sedan, Truck, SUV).
019d8422get drives
Returns a list of all drive types (e.g., FWD, AWD, RWD).
019d8422get engines
Returns a list of all engine types (e.g., V6, Turbo, Electric).
019d8422get makes
Lists all car manufacturers. You can also filter by year to narrow the results.
019d8422get models
Lists car models for a specific manufacturer, optionally filtering by year.
019d8422get transmissions
Returns a list of all transmission types (e.g., CVT, 6-speed manual).
019d8422get vehicles
Searches the database for vehicles using filters like year, make, model, and body type. It returns detailed specs, including MSRP and fuel economy.
019d8422get years
Returns a list of all available years in the database for filtering searches.
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
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- Publish to catalog or keep private
Make Your AI Do More
Start with CarAPI, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
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- New servers added to the catalog every week
What you can do with this MCP connector
This server lets your agent search over 66,000 vehicles. You can use it to filter cars by year, make, model, body type, engine specs, transmission, and pricing data. You'll get full specs—including MSRP, fuel economy, and dimensions—for virtually any vehicle from 1990 to today.
To start, your agent can get a list of all available years using get_years. You can also get a list of all manufacturers with get_makes, or check out every possible car body type with get_bodies, or see every drive type with get_drives. If you want to narrow down the search, your agent can list all car models for a specific manufacturer and year using get_models.
To see all possible transmissions, just call get_transmissions. You can check out what kind of engines are available by calling get_engines.
Need to find a specific car? Your agent uses get_vehicles to search the database with filters like year, make, model, and body type, returning detailed specs, MSRP, and fuel economy. You can also get a list of all car manufacturers using get_makes and then narrow down your search by year.
When you're ready to build something, your agent can use get_models to list specific car models for a manufacturer and year. It can search for vehicles using get_vehicles to pull the full specs and pricing data. It's a massive resource for anyone who needs accurate, structured automotive data.
How CarAPI MCP Works
- 1 Subscribe to the server and input your CarAPI API Key.
- 2 Ask your AI client (Claude, Cursor, etc.) to find vehicle information, specifying criteria like '2023 SUV' or 'V6 engine'.
- 3 Your agent executes the necessary tools in sequence—for instance, using
get_makesandget_models—to callget_vehiclesand deliver the full data set.
The bottom line is, your agent handles the complex multi-step data lookups, so you just talk to it.
Who Is CarAPI MCP For?
Anyone dealing with automotive data needs this. It's for car shoppers who want to compare specs across years and brands, for dealership staff who need to quickly verify inventory data, and for developers building comparison tools. If your job involves anything more complex than looking up a single car's year and make, you need this.
Uses the server to pull reference lists (e.g., all body types, all transmissions) and cross-reference them against specific vehicle records to build reports.
Verifies vehicle specs, checks if a model is available for a given year, or cross-references trim levels against the central database.
Integrates the tools to build a multi-filter car configurator or comparison widget that needs accurate, structured automotive data.
What Changes When You Connect
- Compare vehicles across years and makes. Use
get_vehiclesto compare a 2020 Civic and a 2020 Mazda 3 side-by-side. It pulls identical data fields (MSRP, engine, MPG) for direct comparison. - Build structured filters. Before searching, use
get_bodies,get_engines, orget_transmissionsto get a definitive list of options. This prevents bad inputs and makes your front-end logic clean. - Scope down the search. Need to know what makes exist for a specific year? Run
get_makeswith the year filter. This immediately narrows the field, saving your agent time and improving response speed. - Get the full picture.
get_vehiclesreturns more than just the name. It includes engine specs, fuel economy (MPG), and the MSRP, giving you the complete data point for a consumer listing. - Reference data is key. Use
get_yearsfirst. This guarantees the year you're searching with is valid, preventing failed API calls and keeping the data flow predictable.
Real-World Use Cases
A shopper needs to compare three cars.
A user asks, 'Show me the specs for a 2022 Subaru Outback, a 2022 Honda CRV, and a 2022 Ford Escape.' The agent chains together get_makes and get_models to validate the existence of the vehicles, then calls get_vehicles three times to pull full specs, MSRP, and MPG for all three, solving the comparison problem in one flow.
A developer needs to validate all possible filters.
The developer runs get_bodies to see all body styles. Next, they run get_engines to see all engine types. They then use these reference lists to build a front-end form, ensuring every dropdown option is valid before a user ever submits a query.
A dealer needs to check a model's engine options.
The agent first uses get_makes and get_models to confirm the vehicle lineage. Then, it calls get_vehicles with filters for the specific make/model/year to see all available trims and their associated engine specs and MSRPs, allowing the dealer to quickly update inventory.
A data analyst needs a reliable year range.
Instead of guessing, the analyst runs get_years. This immediately gives them the official start and end year range of the database (e.g., 1990-2024), ensuring any subsequent searches for makes or models are scoped correctly.
The Tradeoffs
Asking for all car data in one go
Prompting: 'Give me all car data.' The agent attempts to call get_vehicles with no filters, hitting an invalid state or returning millions of records, which is unusable.
→
Start small. First, use get_years to find the range. Then, use get_makes with that year range to limit the options. Finally, use get_models to drill down until you get the specific vehicle you need.
Assuming a filter exists
The user asks for '2025 electric pickup.' The agent tries to call get_vehicles but fails because the data set doesn't contain 2025 records yet, returning a dead end.
→
Always validate the year first. Run get_years to confirm the search window. If the desired year isn't listed, you know the search will fail before you even try to get makes or models.
Mixing up component types
Trying to filter by a 'Drive Type' (like AWD) and a 'Body Type' (like Sedan) but only providing the name without the proper reference list calls. The agent might use the wrong data structure.
→
Build your query using the reference tools. Call get_bodies to get the correct body type name, and then call get_drives to get the correct drive type name. Use these validated inputs when calling get_vehicles.
When It Fits, When It Doesn't
Use this server if your goal is to build any application that requires structured, detailed automotive specifications (engine power, MPG, MSRP, etc.). It's ideal for comparison tools, configurators, and inventory lookup systems. Don't use it if you only need general marketing data, like 'how many cars sold last year.' For that, you need a sales reporting or market analysis tool. If you only need a list of manufacturers without any vehicle data, get_makes works, but you'll need to follow up with get_models to get usable results.
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 server provides 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Finding car specs shouldn't feel like running a database query.
Today, building a car comparison tool means having multiple steps: a user picks a year, you hit the Make API endpoint. Then you hit the Model API, and finally, you hit the Specs API. Every step is a separate network call, and if you miss one piece of data—like the correct body type ID—the whole thing breaks and you have to restart the whole process.
With this MCP Server, your agent handles the whole chain. You tell it: 'Compare three cars.' It runs the necessary sequence of tool calls (`get_makes`, `get_models`, `get_vehicles`) behind the scenes. You get the structured, comparison-ready data set without managing any tool calls yourself.
CarAPI MCP Server: Get full vehicle specs and pricing
The manual steps that vanish are the need to manage tool dependencies. You don't write code to call `get_makes` before `get_models`. You don't have to worry about whether the year you're checking is valid. The agent manages the complex flow, giving you reliable data every time.
It's about the data structure. You get a single, clean output that is ready to drop into a comparison table or a report. That's the difference between having a pile of raw data points and having a finished product.
Common Questions About CarAPI MCP
How do I use the get_vehicles tool with multiple filters? +
The agent handles multiple filters automatically. Just prompt it with the criteria, like '2024 electric SUV with AWD.' The agent knows to pass '2024', 'electric', 'SUV', and 'AWD' to the get_vehicles tool.
Do I need to use get_years before getting_makes? +
It's best practice to check the year first. Running get_years confirms the available range (e.g., 1990-2024) before you try to get makes for a specific year, preventing failed lookups.
What is the difference between get_models and get_vehicles? +
get_models only gives you a list of model names for a given manufacturer and year. get_vehicles gets the full, detailed records, including MSRP, engine specs, and fuel economy for those models.
Can I filter by body type and engine type? +
Yes. You can ask the agent to filter by both. It will use the reference tools (get_bodies, get_engines) to validate the inputs and pass them into the get_vehicles tool.
How do I find the list of all car manufacturers using the `get_makes` tool? +
The get_makes tool returns a list of all available car manufacturers and their IDs. You can use this list to narrow down your searches or see which brands are supported in the database.
What happens if I use `get_vehicles` without specifying a year? +
The get_vehicles tool will search across all years available in the database. You can then further refine the results using filters like make, model, or body type.
Can I use `get_bodies` to understand the available body styles? +
Yes, the get_bodies tool provides a complete reference list of body types (like Sedan, SUV, Truck, etc.). Use this list to ensure your filtering criteria are accurate.
Is there a limit to how many vehicles I can check with `get_vehicles`? +
The tool supports searching 66,000+ vehicles. While we don't list a hard limit, you can process large datasets by iterating through filters or asking for results in batches.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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