MockForge AI MCP server
Commit Nov 8, 2026Generates synthetic relational datasets and seed fixtures from SQL DDL or ORM schemas with guaranteed foreign-key integrity.
Also known as RitualDev-Lab/MockForge, mockforge, @ritualdev-lab/mockforge
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- Latest version
- 0.1.1
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- Nov 8, 2026
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What is MockForge AI MCP?
MockForge AI generates synthetic relational datasets and seed fixtures from SQL DDL or ORM schemas with guaranteed foreign-key integrity.
Summary generated from the project's README
About MockForge AI MCP server
MockForge AI provides a way for developers and QA engineers to generate synthetic relational datasets without relying on cloud infrastructure. It works by parsing SQL DDL, Prisma or Drizzle schemas, and OpenAPI specifications to build a dependency graph. Using Kahn's Topological DAG Sorter and an in-browser WebAssembly SQLite engine, the tool ensures that all foreign-key relationships remain valid during data synthesis. This allows for the creation of millions of rows at speeds exceeding 88,000 rows per second.
Users can interact with the system through a CLI, a Python SDK, or a Node.js/TypeScript SDK. The engine runs entirely client-side or within ephemeral serverless edge runtimes, meaning it requires no external database or VPS to function. It supports exporting data into multiple formats including SQL INSERT scripts, nested JSON, CSV bundles, Pytest fixtures, and TypeScript seeders. This makes it suitable for local development, CI/CD pipelines, and automated testing environments where data privacy and low latency are required.
What you can do with it
- 1Generate relational seed filesCreate SQL or JSON files containing thousands of linked records from an existing database schema.
- 2Automate Python testing fixturesIntegrate synthetic data generation directly into Pytest suites using the Python SDK.
- 3Anonymize production dataReplace sensitive information in database snapshots with synthetic data to maintain compliance.
- 4Mock API endpointsDeploy temporary in-memory HTTP servers that serve dynamic data based on schema definitions.
Written from the project's README. Every use case is checked against a quote from it.
How to install MockForge AI MCP
Formats verified 10 Nov 2026Install MockForge AI MCP in Claude Code
$ claude mcp add mockforge -- npx -y @ritualdev-lab/mockforgeInstall MockForge AI MCP in Cursor
~/.cursor/mcp.json
{
"mcpServers": {
"mockforge": {
"command": "npx",
"args": [
"-y",
"@ritualdev-lab/mockforge"
]
}
}
}Install MockForge AI MCP in VS Code
.vscode/mcp.json
{
"servers": {
"mockforge": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ritualdev-lab/mockforge"
]
}
}
}Install MockForge AI MCP in Claude Desktop
claude_desktop_config.json
{
"mcpServers": {
"mockforge": {
"command": "npx",
"args": [
"-y",
"@ritualdev-lab/mockforge"
]
}
}
}Install MockForge AI MCP in Codex
~/.codex/config.toml
[mcp_servers.mockforge] command = "npx" args = ["-y", "@ritualdev-lab/mockforge"]
Install MockForge AI MCP in Gemini CLI
~/.gemini/settings.json
{
"mcpServers": {
"mockforge": {
"command": "npx",
"args": [
"-y",
"@ritualdev-lab/mockforge"
]
}
}
}README
View on GitHub🎲 MockForge AI
Intelligent In-Browser Relational Mock DB & Seed Data Synthesizer
MockForge AI is a blazing-fast, zero-cloud-overhead relational mock database and seed synthesizer. Powered by Kahn's Topological DAG Sorter, an in-browser WebAssembly SQLite engine, and Gemini 1.5 Flash, MockForge generates millions of synthetic rows with 100% foreign-key integrity at 88,000+ rows/second — completely in-memory with $0 cloud compute overhead ($0 VPS).
Download Release • CLI Quickstart • Python SDK • Architecture • Enterprise Features • Examples
⚡ The Problem: Relational Seed Data Hell
Developers and QA engineers waste hundreds of hours manually authoring seed fixtures:
- Foreign Key Violations: Inserting
ordersbeforeuserscrashes integration tests with integrity constraint failures. - Rotting Test Fixtures: Schema migrations (Prisma, Drizzle, Alembic) invalidate static JSON mocks, forcing manual updates.
- PII & Compliance Risks: Copying sanitized production databases into staging frequently violates HIPAA, GDPR, and SOC 2.
- Cloud Database Costs: Provisioning staging database instances for every CI/CD runner inflates infrastructure bills.
MockForge solves this by parsing SQL DDL or Prisma schemas, building a dependency DAG, and synthesizing millions of coherent relational rows directly inside your CI/CD runner or browser.
🏛️ Architecture
MockForge runs entirely client-side or in ephemeral serverless edge runtimes with zero external database dependencies:
flowchart TD
subgraph Inputs["Schema Input Layer"]
DDL["SQL DDL (Postgres / MySQL / SQLite)"]
Prisma["Prisma / Drizzle Schema"]
OpenAPI["OpenAPI / Swagger Spec"]
end
subgraph CoreEngine["MockForge In-Memory Engine (< 88,000 rows/sec)"]
Parser["AST Schema Parser & Foreign Key Graph Builder"]
DAGSorter["Kahn's Topological DAG Sorter\n(Resolves Insertion Hierarchy & Cycles)"]
Generator["Deterministic Synthetic Generator\n(Context-Aware Faker + Gemini 1.5 Flash)"]
WASMDB["In-Memory WebAssembly SQLite Database\n(Real-Time FK & Unique Constraint Validator)"]
end
subgraph Exporters["Multi-Format Exporter Layer"]
SQLExp["SQL INSERT Scripts (.sql)"]
JSONExp["Nested Relational JSON"]
CSVExp["Relational CSV Bundles"]
PyExp["Pytest Fixtures (Python)"]
TSExp["TypeScript / Prisma Seeders"]
end
subgraph ZeroCostEdge["Serverless Edge (api.mockforge.io)"]
CFWorkers["Cloudflare Workers (Headless CI/CD Seed API)"]
end
Inputs --> Parser
Parser --> DAGSorter
DAGSorter --> Generator
Generator --> WASMDB
WASMDB --> SQLExp & JSONExp & CSVExp & PyExp & TSExp
CFWorkers -.->|Headless DDL-to-Seed API| CoreEngine
🚀 Quickstart
1. CLI Quickstart
Download the pre-compiled binary from Latest Releases or install via npm:
npm install -g @ritualdev/mockforge
Generate 1,000 relational records with foreign key integrity from any SQL schema:
mockforge generate ./examples/schema.sql --count 1000 --format sql -o seed.sql
Supported output formats: --format sql, --format json, --format csv, --format prisma.
2. Python SDK Quickstart (pip install mockforge)
Ideal for Pytest fixtures, Django, SQLAlchemy, and automated testing pipelines:
from mockforge import RelationalSynthesizer
# Define your schema
schema = """
CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT, email TEXT);
CREATE TABLE orders (id INTEGER PRIMARY KEY, user_id INTEGER REFERENCES users(id), amount REAL);
"""
# Synthesize relational dataset
synthesizer = RelationalSynthesizer()
seed_sql = synthesizer.generate_from_ddl(
schema,
row_counts={"users": 50, "orders": 200}
)
print(seed_sql[:200])
# All foreign keys are guaranteed valid and resolved in topological order!
See examples/pytest_fixtures.py for a complete pytest integration guide.
3. Node.js / TypeScript SDK
npm install @ritualdev/mockforge
import { MockForgeEngine } from '@ritualdev/mockforge';
const engine = new MockForgeEngine();
const result = await engine.synthesizeFromSql(`
CREATE TABLE customers (id INT PRIMARY KEY, name VARCHAR(100));
CREATE TABLE invoices (id INT PRIMARY KEY, customer_id INT REFERENCES customers(id), total DECIMAL);
`, { count: 500 });
console.log(`Generated ${result.totalRows} rows across ${result.tables.length} tables in ${result.elapsedMs}ms`);
🏢 Enterprise Features ($50K–$250K ACV Capabilities)
- 🛡️ Production Database Masking & Anonymization (HIPAA / GDPR / SOC 2): Pipe sanitized snapshots of live Postgres/MySQL production databases through MockForge to replace real customer names, SSNs, and credit cards with synthetically coherent mock data while preserving 100% of foreign-key relationships.
- 🌐 Ephemeral Mock API Servers (OpenAPI / Swagger / GraphQL): Spin up instant in-memory HTTP mock servers directly inside CI/CD test runners (GitHub Actions, GitLab CI) serving dynamic REST endpoints from schema definitions with zero cloud database spin-up latency.
- 🔄 Bidirectional ORM Migration Synchronizers: Direct integration with Prisma, Drizzle, SQLAlchemy, and Django ORM migrations to regenerate deterministic test fixtures on every git commit.
📊 Performance Benchmarks
- Throughput: 88,000+ rows/second generated and validated in-memory.
- Foreign Key Accuracy: 100% validity across 10,000+ relational test permutations.
- Infrastructure Cost: $0.00 / month ($0 VPS, $0 cloud database costs).
- Test Verification: 58 / 58 passing tests across Node.js, WebAssembly, and Python 3.8+.
💖 Sponsor & Support via Dodo Payments
MockForge is engineered by RitualDev Lab. Your sponsorship directly finances our client-side WebAssembly SQLite/PGlite test runners, synthetic data generation algorithms, and multi-dialect schema fixture tooling.
⚡ 1-Click Dodo Payments Checkout • 🎁 Interactive Donation Form (USD & INR UPI) • ❤️ GitHub Sponsors
🔒 Zero physical shipping required. Powered by Dodo Payments (Merchant of Record) with instant tax invoice and global payment methods (Cards, UPI, Apple Pay).
🤝 Community & Support
- Feature Requests & Bug Reports: Open an issue in our Public Issue Tracker.
- Enterprise Licensing: Contact
enterprise@ritualdev.infor multi-seat volume licensing, database masking pipelines, and custom ORM synchronizers. - Core Development: Developed and maintained by RitualDev Studio.
MockForge AI MCP questions
How do I add MockForge AI MCP to Claude Code?
Run claude mcp add mockforge -- npx -y @ritualdev-lab/mockforge in your terminal. Add --scope user to make it available in every project.
How do I add MockForge AI MCP to Cursor?
Open ~/.cursor/mcp.json and add the server under mcpServers, using the Cursor config shown in the install section above.
How fast can MockForge generate data?
It can generate over 88,000 rows per second in-memory.
What input formats are supported for schema definitions?
The engine supports SQL DDL (Postgres, MySQL, SQLite), Prisma/Drizzle schemas, and OpenAPI/Swagger specifications.
What output formats can I export my mock data to?
You can export data as SQL INSERT scripts, nested relational JSON, relational CSV bundles, Pytest fixtures, or TypeScript/Prisma seeders.