Menghasilkan dataset relasional sintetis dan seed fixtures dari SQL DDL atau skema ORM dengan jaminan integritas foreign-key.
Juga dikenal sebagai RitualDev-Lab/MockForge, mockforge, @ritualdev-lab/mockforge
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- Unduhan, 30 hari
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- Versi terbaru
- 0.1.1
- Commit terakhir
- 8 Nov 2026
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Apa itu MockForge AI MCP?
MockForge AI adalah MCP server. Menghasilkan dataset relasional sintetis dan seed fixtures dari SQL DDL atau skema ORM dengan jaminan integritas foreign-key.
Ringkasan dibuat dari README proyek
Tentang MockForge AI MCP server
MockForge AI menyediakan cara bagi developer dan QA engineer untuk menghasilkan dataset relasional sintetis tanpa bergantung pada infrastruktur cloud. Tool ini bekerja dengan memparsing SQL DDL, skema Prisma atau Drizzle, dan spesifikasi OpenAPI untuk membangun dependency graph. Dengan menggunakan Kahn's Topological DAG Sorter dan engine WebAssembly SQLite di dalam browser, tool ini memastikan semua relasi foreign-key tetap valid selama proses sintesis data. Hal ini memungkinkan pembuatan jutaan baris data dengan kecepatan melebihi 88.000 baris per detik.
Pengguna dapat berinteraksi dengan sistem melalui CLI, Python SDK, atau Node.js/TypeScript SDK. Engine ini berjalan sepenuhnya di sisi client atau di dalam ephemeral serverless edge runtimes, yang berarti tidak memerlukan database eksternal atau VPS untuk berfungsi. Tool ini mendukung ekspor data ke berbagai format termasuk SQL INSERT scripts, nested JSON, CSV bundles, Pytest fixtures, dan TypeScript seeders. Ini membuatnya sangat cocok untuk pengembangan lokal, pipeline CI/CD, dan lingkungan automated testing yang membutuhkan privasi data serta latensi rendah.
Apa yang bisa Anda lakukan dengannya
- 1Generate relational seed filesMembuat file SQL atau JSON yang berisi ribuan record yang saling terhubung dari skema database yang sudah ada.
- 2Automate Python testing fixturesMengintegrasikan pembuatan data sintetis secara langsung ke dalam suite Pytest menggunakan Python SDK.
- 3Anonymize production dataMengganti informasi sensitif pada snapshot database dengan data sintetis untuk menjaga kepatuhan (compliance).
- 4Mock API endpointsMenjalankan temporary in-memory HTTP servers yang menyajikan data dinamis berdasarkan definisi skema.
Ditulis berdasarkan README proyek. Setiap use case diperiksa terhadap kutipan dari README tersebut.
Cara menginstal MockForge AI MCP
Format diverifikasi 10 Nov 2026Tambahkan ke CursorTambahkan ke GooseInstal MockForge AI MCP di Claude Code
$ claude mcp add mockforge -- npx -y @ritualdev-lab/mockforgeInstal MockForge AI MCP di Cursor
~/.cursor/mcp.json
{
"mcpServers": {
"mockforge": {
"command": "npx",
"args": [
"-y",
"@ritualdev-lab/mockforge"
]
}
}
}Instal MockForge AI MCP di VS Code
.vscode/mcp.json
{
"servers": {
"mockforge": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ritualdev-lab/mockforge"
]
}
}
}Instal MockForge AI MCP di VS Code CLI
$ code --add-mcp '{"name":"mockforge","type":"stdio","command":"npx","args":["-y","@ritualdev-lab/mockforge"]}'Instal MockForge AI MCP di Claude Desktop
claude_desktop_config.json
{
"mcpServers": {
"mockforge": {
"command": "npx",
"args": [
"-y",
"@ritualdev-lab/mockforge"
]
}
}
}Instal MockForge AI MCP di Codex
$ codex mcp add mockforge -- npx -y @ritualdev-lab/mockforgeInstal MockForge AI MCP di Codex config.toml
~/.codex/config.toml
[mcp_servers.mockforge] command = "npx" args = ["-y", "@ritualdev-lab/mockforge"]
Instal MockForge AI MCP di Gemini CLI
$ gemini mcp add -s user mockforge npx -- -y @ritualdev-lab/mockforgeInstal MockForge AI MCP di Gemini settings.json
~/.gemini/settings.json
{
"mcpServers": {
"mockforge": {
"command": "npx",
"args": [
"-y",
"@ritualdev-lab/mockforge"
]
}
}
}Pengecekan otomatis
Berjalan 15 Nov 2026Laporan lengkap →Lolos checks otomatis kami. Ini bukan audit keamanan: baca kodenya dan berikan server akses paling terbatas yang dibutuhkannya. Cara kerja checks · Yang perlu diperbaiki untuk menambah poin
- Source repository terhubung dan dapat dijangkauritualdev-lab/mockforge is public and reachablegithub.com/ritualdev-lab/mockforge ↗Lolos
- Nama paket mencantumkan repositori ini sebagai sumbernya@ritualdev-lab/mockforge names ritualdev-lab/mockforge as sourcenpmjs.com/package/@ritualdev-lab/mockforge ↗Lolos
- Provenance build (atestasi Sigstore)No build attestation published for this releaseTidak diperiksa
- Lisensi open source (OSI)No licenseTemuan
- Ada commit atau release dalam satu tahun terakhirLast commit or release 2026-11-08github.com/ritualdev-lab/mockforge ↗Lolos
- OpenSSF ScorecardTidak diperiksa
- Nama package tidak mirip dengan package populerNo popular package one edit away from @ritualdev-lab/mockforgeLolos
- Tidak ada advisory kritis pada paket atau dependensinya99 resolved dependencies checked; no critical advisoryosv.dev ↗Lolos
- Tidak ada instruksi tersembunyi untuk model0 tool descriptions and the README read; no override phrases, credential paths or hidden charactersLolos
- Tools yang bersifat destruktif telah diberi labelTidak diperiksa
- Endpoint remote merespons handshake MCPTidak diperiksa
README
Lihat di 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.
Pertanyaan tentang MockForge AI MCP
Bagaimana cara menambahkan MockForge AI MCP ke Claude Code?
Jalankan claude mcp add mockforge -- npx -y @ritualdev-lab/mockforge di terminal Anda. Tambahkan --scope user agar tersedia di semua proyek.
Bagaimana cara menambahkan MockForge AI MCP ke Cursor?
Buka ~/.cursor/mcp.json dan tambahkan server di bawah mcpServers, menggunakan config Cursor yang ditunjukkan di bagian instalasi di atas.
Seberapa cepat MockForge dapat menghasilkan data?
MockForge dapat menghasilkan lebih dari 88.000 baris per detik secara in-memory.
Format input apa saja yang didukung untuk definisi skema?
Engine ini mendukung SQL DDL (Postgres, MySQL, SQLite), skema Prisma/Drizzle, serta spesifikasi OpenAPI/Swagger.
Format output apa saja yang tersedia untuk ekspor data mock saya?
Anda dapat mengekspor data sebagai SQL INSERT scripts, nested relational JSON, relational CSV bundles, Pytest fixtures, atau TypeScript/Prisma seeders.