# mongodb

Equips coding agents with MongoDB schema design patterns, query operators, and document modeling strategies for NoSQL workloads.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 341 installs · ★ 78
- Author: Claude MPM Team
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/bobmatnyc/claude-mpm-skills/tree/HEAD/toolchains/databases/mongodb/SKILL.md
- Updated: 2026-07-18
- HTML: https://affpapa.org/en/nevibecode/skills/claude-mpm-team-mongodb

## What it does

This skill guides assistants through MongoDB schema architecture. It details when to embed child documents for bounded relations versus when to normalize using object references. It also covers the extended reference pattern, keeping frequently displayed parent fields inside child records for faster reads.

Query syntax patterns cover standard CRUD, projections, array matching, and logical filters. Unbounded document growth breaks applications due to the 16MB BSON ceiling, so the guide explicitly flags when to split collections. Complex multi-table joins or heavy multi-document ACID transactions remain poor fits for this architecture.

## Requirements
- Works with: MongoDB

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add bobmatnyc/claude-mpm-skills --skill mongodb -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/mongodb/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/mongodb/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add bobmatnyc/claude-mpm-skills --skill mongodb -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/mongodb/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add bobmatnyc/claude-mpm-skills --skill mongodb -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/mongodb/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install bobmatnyc/claude-mpm-skills mongodb
```

2. **Manually: put the files in place**
- File: `.github/skills/mongodb/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add bobmatnyc/claude-mpm-skills --skill mongodb -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/bobmatnyc/claude-mpm-skills --path toolchains/databases/mongodb
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/mongodb/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add bobmatnyc/claude-mpm-skills --skill mongodb -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/bobmatnyc/claude-mpm-skills/tree/HEAD/toolchains/databases/mongodb/SKILL.md

## When to use it
- decide between embedded and referenced document schemas
- structure CRUD queries using comparison, element, and array operators
- prevent documents from hitting the 16MB BSON size limit
- implement extended reference patterns for read-heavy collections

## Example prompts
- Design a MongoDB schema for an e-commerce order system with items and addresses
- Write an upsert query in MongoDB that sets a timestamp when inserting a record
- Explain whether I should reference or embed comments on a blog post collection

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 8749941519de0706.

BEGIN THIRD-PARTY FILE 8749941519de0706
````text
---
name: mongodb
description: MongoDB - NoSQL document database with flexible schema design, aggregation pipelines, indexing strategies, and Spring Data integration
user-invocable: false
disable-model-invocation: true
version: 1.0.0
category: toolchain
author: Claude MPM Team
license: MIT
progressive_disclosure:
  entry_point:
    summary: "Document-oriented NoSQL database with flexible schemas, powerful aggregation framework, horizontal scaling, and rich query language"
    when_to_use: "Building apps with flexible/evolving schemas, hierarchical data, real-time analytics, geospatial queries, or need horizontal scaling"
    quick_start: "1. Design documents (embedded vs referenced) 2. Create indexes for queries 3. Use aggregation for analytics 4. Implement proper connection pooling"
context_limit: 700
tags:
  - mongodb
  - nosql
  - database
  - document-database
  - aggregation
  - indexing
  - spring-data
  - schema-design
requires_tools: []
---

# MongoDB - Document Database Patterns

## Overview

MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents. It excels at handling unstructured or semi-structured data, hierarchical relationships, and scenarios requiring horizontal scaling.

**Key Features**:
- Flexible schema (schemaless documents)
- Rich query language with secondary indexes
- Aggregation framework for analytics
- Horizontal scaling (sharding)
- Replica sets for high availability
- Change streams for real-time data
- Geospatial and full-text search

**When to Use MongoDB**:
- Rapidly evolving schemas
- Hierarchical/nested data (embedded documents)
- Real-time analytics with aggregation
- Geospatial applications
- Content management systems
- IoT data ingestion
- Catalog/inventory systems

**When NOT to Use MongoDB**:
- Complex multi-table joins (use RDBMS)
- ACID transactions across many documents (improved in 4.0+, but limited)
- Strict schema enforcement requirements

## Schema Design Fundamentals

### Document Structure

```javascript
// MongoDB document (BSON format)
{
  "_id": ObjectId("507f1f77bcf86cd799439011"),  // Primary key (auto-generated)
  "email": "alice@example.com",
  "name": "Alice Johnson",
  "profile": {                                   // Embedded document
    "bio": "Software developer",
    "avatar": "https://example.com/avatar.jpg",
    "social": {
      "twitter": "@alice",
      "github": "alice-dev"
    }
  },
  "tags": ["developer", "python", "mongodb"],    // Array field
  "createdAt": ISODate("2024-01-15T10:30:00Z"),
  "updatedAt": ISODate("2024-01-20T14:22:00Z")
}
```

### Embedded vs Referenced Documents

**Embedded (Denormalized)** - Store related data in same document:

```javascript
// Embedded: Good for 1:1 or 1:Few relationships
// User with embedded address
{
  "_id": ObjectId("..."),
  "name": "Alice",
  "address": {
    "street": "123 Main St",
    "city": "San Francisco",
    "zipCode": "94102"
  }
}

// Embedded: Order with line items (1:Many bounded)
{
  "_id": ObjectId("..."),
  "orderNumber": "ORD-2024-001",
````
END THIRD-PARTY FILE 8749941519de0706

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