# Random User Generator MCP > Random User Generator provides high-fidelity, mock user profiles for testing and development. Get realistic datasets—including full names, emails, profile photos, location data, and more—directly from your AI client. You can generate up to 5,000 unique accounts in bulk or lock down specific users using a seed for consistent testing across runs. ## Overview - **Category:** productivity - **Price:** Free - **Tags:** mock-data, user-generation, testing, prototyping, api-testing ## Description Listen up. The **`generate_users`** tool is your go-to for mock data. It lets your AI client spit out realistic user profiles—the kind you actually need when testing an app, not the fake junk from some basic API. You get full records here: names, emails, profile pictures, location details, and everything else that makes a dataset feel real. If you're running tests or building a prototype and you need a ton of data, this thing handles it. You can run bulk user generation, processing up to 5,000 accounts in one shot. That’s huge for stress testing your database or seeding out massive staging environments without having to write half a dozen scripts. It just works. But here's the real kicker: consistency. You know how bad it is when test data changes every time you run a script? Not with this tool. You can lock down specific users using a seed string. This means that no matter how many times your agent runs the function, the dataset will be identical. It guarantees consistent testing across all your development cycles. That's non-negotiable for solid QA. When you need to narrow things down—say, you're only testing features for users in Berlin or women from Canada—you can filter by both nationality and gender. You tell it the criteria, and it just gives you a pool of users that match up perfectly. It’s precise; no random noise there. And don't forget about controlling the payload. This tool lets you select exactly which data fields you want to include or exclude. Need to test only if your login screen handles emails and usernames? You tell it to pull just those two things, keeping your resulting dataset lean and clean. It keeps the output focused on what matters for that specific feature check. The `generate_users` tool can combine all these functions. You're not stuck generating random garbage; you can filter by a country *and* a gender demographic while simultaneously running it with a seed to make sure those results are reproducible, and then limiting the output payload to just names and emails. It's that much control over realistic data generation. ## Tools ### generate_users Generates random user data. It lets you filter by gender, nationality, and use a seed to keep results consistent. ## Prompt Examples **Prompt:** ``` Generate 5 random female users from Germany and the UK. ``` **Response:** ``` I've generated 5 female profiles from DE and GB. Each includes full names, emails, and profile pictures. Would you like to see the specific location details for these users? ``` **Prompt:** ``` Create 10 random users but only include their name, email, and nationality. ``` **Response:** ``` I've fetched 10 user profiles filtered to show only names, emails, and nationalities as requested. This keeps the data payload clean for your prototype. ``` **Prompt:** ``` Generate a random user using the seed 'vurb-testing' for deterministic results. ``` **Response:** ``` Using the seed 'vurb-testing', I've generated a profile for 'Jennie Nichols'. You can use this same seed anytime to retrieve this exact user data again. ``` ## Capabilities ### Generate mock user profiles Creates full records including names, emails, profile pictures, and location details. ### Run bulk user generation Processes up to 5,000 accounts in a single API call for large-scale testing. ### Lock data with seeds Uses specific seed strings to ensure the generated dataset remains identical on every run, guaranteeing test consistency. ### Filter by nationality and gender Narrows the output pool so you only get users matching a specified country or gender demographic. ### Select custom data fields Controls the payload, allowing you to include or exclude specific information like just emails and names. ## Use Cases ### Testing internationalized forms The QA team needs to validate user sign-up flow for Brazil and Germany. Instead of finding two separate data sources, they ask their agent to run `generate_users` filtering by 'BR' and 'DE'. They get a combined dataset instantly, ensuring the form handles diverse names and formats correctly. ### Building a high-fidelity prototype A product designer needs 12 different mock profiles for a new dashboard mockup. She prompts her agent to 'Generate 12 random users' and then uses the output to fill out profile cards, giving stakeholders a diverse view of potential customers. ### Replicating a failed test case A developer discovers a bug that only happens with User ID 'xyz'. They use `generate_users` and provide the specific seed value from when they first encountered the bug. The agent regenerates the exact user profile, making replication simple and fast. ### Seeding a staging database The DevOps team needs 5,000 dummy records for load testing before launch. They use `generate_users` to hit the bulk generation limit, creating a massive, realistic dataset in one go without manually managing API calls. ## Benefits - Bulk generation handles the heavy lifting. You can request up to 5,000 user profiles at once for stress testing or database seeding—no need to run multiple small requests. - You get data consistency right out of the box. Use a specific seed value so your test runs always pull the exact same set of users, which is critical for debugging UI bugs. - Localization filters save time. Need US-based users? Or only Canadian profiles? You filter by nationality to match the target demographic instantly. - The payload stays clean. By choosing custom fields, you strip out unnecessary data (like old contact info) and keep your mock dataset lean for easy consumption. - It works with all your agents. Whether you use Claude, Cursor, or a Python agent, connecting through MCP makes the process uniform: just send the prompt. ## How It Works The bottom line is: You send plain text instructions to your AI client, and it handles the API connection to deliver clean user data back to you. 1. Subscribe to this server. No API key is required for the public service. 2. Tell your AI client (Claude, Cursor, etc.) exactly what data you need—e.g., 'Generate 10 users from Japan.' 3. Your agent calls the tool, and it returns a structured list of profiles matching your filters. ## Frequently Asked Questions **Can I generate users from specific countries like the US or France?** Yes! Use the `generate_users` tool and provide a comma-separated list of nationalities in the `nat` parameter (e.g., 'US,FR'). **How do I ensure I get the same random users every time I run a test?** You can use the `seed` parameter in the `generate_users` tool. Providing the same string as a seed will return the exact same set of user data for deterministic testing. **Is it possible to exclude sensitive fields like login passwords from the response?** Absolutely. Use the `exc` parameter to list fields you want to exclude (e.g., 'login'), or use `inc` to specify only the fields you need (e.g., 'name,email,picture'). **What is the maximum number of users I can generate with a single call to `generate_users`?** You can create up to 5,000 user profiles in one request. This bulk limit makes it ideal for performance testing and large-scale database seeding. **Do I need an API key or special credentials to connect the Random User Generator MCP Server?** No, you don't. The server uses a public service connection, so no API key is required for your AI client to start generating mock data. **If I need more than 5,000 users, how does `generate_users` handle pagination?** You navigate large datasets using page numbers combined with seeds. This allows you to reliably move through massive amounts of generated data without losing context. **Which AI clients can I connect to the Random User Generator MCP Server from?** Any client compatible with the Model Context Protocol (MCP) will work. You just need to point your agent—whether it's Claude, Cursor, or something else—to the Vinkius Marketplace. **What happens if my call to `generate_users` uses an invalid nationality filter?** The server returns a specific error message detailing why the request failed. This immediate feedback helps you correct your parameters and retry the generation quickly.