Faker MCP. Generate any structured mock data type instantly.
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…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Faker. Generate realistic, mock data for development and testing. Instantly populate databases, test APIs, or build UI mockups with structured, placeholder data—including addresses, people, companies, and credit cards—all generated on demand.
What your AI agents can do
Get addresses
Generates mock address data, with the option to filter by a specific country code.
Get books
Generates mock book records, including titles and author names.
Get companies
Generates mock company data, perfect for populating business directories.
Calls get_addresses to return structured, fake street addresses, allowing filtering by country code.
Calls get_books to return structured, fake book information, including titles and authors.
Calls get_companies to return structured, fake business data, including VAT numbers and websites.
Calls get_credit_cards to return safe, non-functional credit card details for testing payment flows.
Calls get_custom to build specific data objects by mapping your desired field names to Faker data types.
Calls get_images to return placeholder image URLs, supporting sources like Picsum or specific types like 'pokemon'.
Calls get_persons to return structured, fake personal information, letting you specify the gender.
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Supported MCP Clients
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Faker MCP Server: 11 Tools for Mock Data Generation
Use these 11 tools to generate any structured data type needed for development, testing, or prototyping.
019e5d18get addresses
Generates mock address data, with the option to filter by a specific country code.
019e5d18get books
Generates mock book records, including titles and author names.
019e5d18get companies
Generates mock company data, perfect for populating business directories.
019e5d18get credit cards
Generates safe, non-functional credit card information for testing payment endpoints.
019e5d18get custom
Creates custom mock data objects by mapping your desired field names to specific Faker types.
019e5d18get images
Generates mock URLs for images, supporting multiple sources and types like 'any' or 'pokemon'.
019e5d18get persons
Generates structured mock personal information, letting you specify if the person is male, female, or other.
019e5d18get places
Generates mock place data, useful for mapping or location-based features.
019e5d18get products
Generates mock product listings, useful for e-commerce testing.
019e5d18get texts
Generates random blocks of text content, replacing dummy filler text.
019e5d18get users
Generates structured mock data for user accounts, including names and credentials.
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 Faker, 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
- 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
What you can do with this MCP connector
You gotta hook up your AI client to the Faker MCP server to instantly spit out realistic, mock data. Need to fill a database, test an API, or mockup a UI? This thing gives you structured, placeholder data—everything you need, on demand.
People and User Info: You can grab structured fake personal details using get_persons, specifying if you want a male, female, or other person. For accounts, get_users spits out structured mock data including names and credentials. You can also get full user accounts using get_users and structured business data with get_companies, which includes VAT numbers and websites.
Addresses and Locations: You can generate mock street addresses with get_addresses, and you can filter those addresses by a specific country code. For location-based features or mapping, get_places spits out mock place data.
Content and Media: get_texts generates random blocks of text, so you don't gotta use 'lorem ipsum' anymore. get_images returns placeholder image URLs, supporting sources like Picsum or specific types like 'pokemon'. You can also get structured mock book records with get_books, which includes titles and authors.
Business and Product Data: For e-commerce testing, get_products generates mock product listings. You can also get mock company data using get_companies, which includes VAT numbers and websites.
Finance and Custom Data: get_credit_cards gives you safe, non-functional credit card details for testing payment flows. If you need a specific data shape, get_custom lets you build custom mock data objects by mapping your desired field names to Faker data types.
General Data: get_users provides mock data for user accounts, while get_persons gives mock personal details. For generic data, get_texts generates random text blocks. You can get mock place data with get_places and mock product listings with get_products.
How Faker MCP Works
- 1 Subscribe to the Faker server. You might need to provide your FakerAPI credentials.
- 2 Prompt your AI client with the data you need. Example: 'I need 5 users and their associated companies in Japan.'
- 3 Your agent calls the appropriate tools (
get_users,get_companies) and returns the structured mock data directly to your workflow.
The bottom line is you get structured, ready-to-use data without ever leaving your AI client.
Who Is Faker MCP For?
Developers, QA Engineers, and Product Designers use this. It's for anyone who needs to fill a system with believable, non-real data—whether you're seeding a database, building a prototype, or writing a test script. It cuts out the time spent generating dummy data manually.
Seeds local databases or mocks API responses for new features, ensuring the code runs against realistic data structures.
Creates diverse, structured test cases quickly, running randomized but valid data through the test suite.
Fills out prototypes and mockups with realistic content (names, addresses, images) instead of placeholder text.
What Changes When You Connect
- Build prototypes with realistic content instead of 'lorem ipsum'. Use
get_textsandget_imagesto fill UI mockups with actual placeholder content, making the design feel final. - Test payment flows safely.
get_credit_cardsreturns non-functional, valid-looking card numbers, letting you verify checkout logic without touching real financial data. - Populate databases with structure. Use
get_companiesandget_usersto generate batches of structured data, including VAT numbers and full user account details. - Handle global localization. The server supports over 70 locales, ensuring that
get_addressesproduces region-specific, authentic data for any country. - Build complex schemas on demand. The
get_customtool lets you define exactly what fields you need (e.g.,{"SKU": "product_code", "owner": "name"}) and generate data matching that exact structure. - Develop full-stack features. Combine tools like
get_persons(for users) andget_products(for inventory) to simulate entire application data sets.
Real-World Use Cases
Need to test a signup flow for a new market.
A developer needs to test user signups in Brazil. They ask their agent to run get_users combined with get_addresses, specifying the Brazilian locale. The agent returns a list of fully formatted user profiles with valid CEP codes, allowing the developer to test the full Brazilian flow.
Mocking a product catalog for a design review.
A product designer needs a catalog mockup. They use get_products and get_companies to generate 20 unique items and the associated company that sells them. This provides enough realistic data for the design team to review the UI without writing a single line of filler text.
Building a complex data migration script.
A QA engineer writes a script that needs 10 records. They use get_custom to define the exact schema needed—say, a JSON object containing {'customer_name': 'name', 'email': 'email', 'transaction_id': 'uuid'}. The agent runs get_custom and gets 10 records matching the required format.
Simulating a complex payment transaction.
A developer needs to test a payment gateway integration. They use get_credit_cards to get a card number, and then get_persons to get a matching name and address. The agent passes all three pieces of data into the payment simulation tool, verifying the entire transaction path.
The Tradeoffs
Using placeholder text everywhere
Filling out a prototype with 'Lorem ipsum dolor sit amet...' because the design team didn't have time to write real dummy content.
→
Use get_texts and get_images. The agent runs these tools to fill the UI with realistic placeholder content, making the mockup look ready for review.
Hardcoding test data for every locale
Manually creating separate test datasets for Germany (DE) and Japan (JP) because the addresses look too different.
→
Use get_addresses and specify the desired country code (e.g., de_DE or ja_JP). The tool generates correctly formatted addresses for any region.
Forgetting the data structure
Getting a list of users and then having to manually adjust the data structure to add a VAT number and a company name, because the initial data was too basic.
→
Use get_custom. Define the required structure using get_custom to map specific fields (like company or vat_number) into a single, consistent JSON object.
When It Fits, When It Doesn't
Use this if you need to populate an application or test suite with structured, believable data. The data must look real—not just random strings. You should use it for testing APIs, seeding databases, or filling out prototypes. Don't use it if you need data that relates to a specific, known external system (like Stripe's real customer list) or if you need to understand data relationships across multiple, non-mocked sources; for that, you need a dedicated data warehouse. If you just need a generic placeholder image, get_images works fine. If you need a full profile, use get_persons or get_users.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Faker API. 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 11 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually generating test data is a huge time sink.
Right now, if you build a new feature, you have to manually create dummy data: a user name, a company name, a random address, and a few images. You copy-paste 'Lorem ipsum' into every text field, and you spend hours just generating the raw content needed to test the UI.
With the Faker server, your agent runs tools like `get_users` and `get_addresses`. It instantly spits out fully structured, locale-specific data, so you can focus on the feature, not the filler content.
Faker MCP Server: Get structured data in seconds.
You no longer have to jump between a fake API site, a database, and a spreadsheet to assemble a single test record. You prompt your agent: 'Give me 5 companies in France.' The agent handles calling `get_companies` and returns the structured JSON immediately.
The process is contained entirely within your AI client. You get structured data, not just a bunch of random strings. It's ready to consume.
Common Questions About Faker MCP
How do I use the get_persons tool with a specific gender? +
You specify the gender in your prompt. The get_persons tool supports 'male', 'female', and 'other' genders, ensuring the data matches your test scenario.
Is the get_credit_cards tool safe for real testing? +
Yes, the get_credit_cards tool generates non-functional, safe numbers. It's designed for testing the payment flow logic, not for processing real transactions.
What is the difference between get_texts and get_custom? +
Use get_texts for simple blocks of filler content. Use get_custom when you need to build a complex, specific data object by mapping multiple fields (like 'product_id' and 'description') to Faker types.
Can I get data for multiple countries using get_addresses? +
Yes, you can filter the get_addresses tool by the country code. This ensures the address format, postal code, and city names are correct for the specific region you target.
Does get_users generate full profiles, or just usernames? +
The get_users tool generates full mock user accounts, including names and credentials, suitable for populating user tables.
How do I ensure my mock data is localized for a specific region using get_addresses? +
You use the _locale parameter. This ensures the addresses match specific country formats, including postal codes and street naming conventions. For example, use the locale code for Germany (de_DE) to get accurate German addresses.
Can I generate mock data for multiple types of entities, like people and companies, in one call? +
No, you call the specific tool for each entity type. You'll use get_persons for personal profiles and get_companies for business data. Each tool manages its own unique data structure.
If I need a highly specific data structure, how should I use the get_custom tool? +
You pass a JSON object mapping field names to Faker types. For instance, you map 'employee_id' to 'uuid' and 'title' to 'job_title'. This lets you build precise mock schemas.
Can I generate data in specific languages like Brazilian Portuguese or French? +
Yes! Use the _locale parameter with codes like pt_BR or fr_FR. This ensures that names, addresses, and other fields follow the conventions of that specific region.
How do I ensure I get the same random data every time for my tests? +
You can use the _seed parameter. By providing the same integer seed in your request, the generator will produce identical results, which is perfect for regression testing.
What is the maximum amount of data I can request in a single call? +
The _quantity parameter allows you to request between 1 and 1000 rows of data per tool execution.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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