faker-data-gen Connector for AI agents.
3 live capabilities
Generate repeatable mock data for E2E testing and database population.
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Why people use faker-data-gen
Deterministic Faker Data Engine for E2E Testing and Mock Data
With this Connector, you just tell your agent how many records you need and provide a seed. It generates thousands of names, addresses, and text blocks in milliseconds. You get a stable, local data source that never changes unless you want it to.
What Vinkius changes
You get instant, repeatable mock data without leaving your local environment.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Flaky UI tests
A QA engineer uses a seed with generate_fake_names so the User Name field doesn't change between every test run.
- Real-world use case 02
Database Seeding
A developer asks the agent to create 1,000 addresses for a new city feature using generate_fake_addresses.
- Real-world use case 03
Content Mocking
A frontend dev needs 20 paragraphs of text to test a blog layout and uses generate_fake_text.
Complete set · 3capabilities
The complete faker-data-gen capability set.
These are the exact actions your AI can choose when you ask it to work with faker-data-gen.
01—03
3 capabilities in this set.
Part of 3 available through faker-data-gen.
- 01 Capability
Generate fake names
Produces a set of synthetic identities using a numeric seed. It's the fastest way to fill a user table with consistent mock names.
- 02 Capability
Generate fake text
Generates paragraphs of lorem ipsum text locally. Use this to fill out content fields in your app without wasting tokens.
- 03 Capability
Generate fake addresses
Creates a list of mock addresses based on a count and a seed. This ensures your location-based tests are perfectly reproducible.
Set up in minutes
One URL. Then ask faker-data-gen to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use faker-data-gen from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_zqXVHiAERlUFEc9cneeKTVPEFZz6uDTR7XoZvaRY/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it faker-data-gen, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable faker-data-gen for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_zqXVHiAERlUFEc9cneeKTVPEFZz6uDTR7XoZvaRY/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the faker-data-gen URL.
- Step 03
Save and start
Save the connection and enable faker-data-gen in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"deterministic-faker-data-engine": {
"url": "https://edge.vinkius.com/vk_preview_zqXVHiAERlUFEc9cneeKTVPEFZz6uDTR7XoZvaRY/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using faker-data-gen
Open Agent mode in chat and ask: "Using faker-data-gen, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"deterministic-faker-data-engine": {
"url": "https://edge.vinkius.com/vk_preview_zqXVHiAERlUFEc9cneeKTVPEFZz6uDTR7XoZvaRY/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using faker-data-gen
Ask Copilot: "Using faker-data-gen, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"deterministic-faker-data-engine": {
"url": "https://edge.vinkius.com/vk_preview_zqXVHiAERlUFEc9cneeKTVPEFZz6uDTR7XoZvaRY/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using faker-data-gen
Open Cascade and ask: "Using faker-data-gen, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"deterministic-faker-data-engine": {
"url": "https://edge.vinkius.com/vk_preview_zqXVHiAERlUFEc9cneeKTVPEFZz6uDTR7XoZvaRY/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using faker-data-gen
Ask Cline: "Using faker-data-gen, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add deterministic-faker-data-engine --transport http "https://edge.vinkius.com/vk_preview_zqXVHiAERlUFEc9cneeKTVPEFZz6uDTR7XoZvaRY/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using faker-data-gen
Ask Claude: "Using faker-data-gen, show me...". 3 tools are ready
Where the request belongs
Work faker-data-gen can move forward.
QA engineers who are tired of flaky tests caused by shifting mock data, and developers who need to populate large databases without risking a PII leak.
QA Engineer
Populates test environments with thousands of unique identities to test pagination and search filters.
SDET
Uses seeds to ensure automated UI tests always interact with the same user names and addresses every run.
Data Privacy Officer
Ensures that no real customer information ever touches the development or testing pipeline.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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RandomUser API
Generate random user data. audit personas and locations via AI.
Random User Generator
Generate high-quality random user data for testing and prototyping. names, emails, and profile photos directly via AI.
Mockaroo
Generate realistic dummy data. audit schemas and field types via AI.
Lorem Ipsum Generator
Generate deterministic placeholder text by words, sentences, or paragraphs. When an AI writes filler text, it varies wildly every time. this engine delivers consistent, predictable lorem ipsum.
Bring your own AI
Change the model, client or framework. Keep faker-data-gen connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about faker-data-gen.
The practical details behind the request, access and result.
Can I use the Deterministic Faker Data Engine to protect user privacy?
Yes, because everything runs locally. It doesn't send your requests to an external service, so your testing intentions and data stay on your machine.
How does the seed work in the Deterministic Faker Data Engine?
The seed ensures that the random number generator produces the same sequence every time. If you use the same seed, you get the exact same names and addresses every single time.
Is the Deterministic Faker Data Engine fast enough for large datasets?
It's extremely fast. It can generate thousands of records in just a few milliseconds because it's a local process, not an API call.
Can I use the Deterministic Faker Data Engine for my CI/CD pipeline?
That's exactly what it's built for. Because it's deterministic, your Playwright or Cypress tests won't fail due to changing mock data.
Does the Deterministic Faker Data Engine provide real addresses?
It generates realistic-looking synthetic addresses. It's perfect for testing UI layouts and database logic without needing real-world accuracy.
Why do I need a 'seed' parameter?
In software testing, you often need the data to be 'fake' but 'repeatable'. If a test fails for user 'John Smith', you want it to generate 'John Smith' again when you re-run the test tomorrow. A seed guarantees mathematical consistency.
Does it use Faker.js under the hood?
No. To maintain the 'zero-dependency' utility promise and keep latency at absolute zero, it relies on a custom, lightweight Linear Congruential Generator (LCG) algorithm built directly into the Connector core.
Is my mock data sent to the cloud?
No. All generation happens locally in your environment. This ensures 100% compliance with strict enterprise development policies.
One connection away
Give your agent a direct line to faker-data-gen.
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