MongoDB Lens

MongoDB Lens is a local Model Context Protocol (MCP) server with full featured access to MongoDB databases using natural language via LLMs to perform queries, run aggregations, optimize performance, and more.

Contents

• Quick Start
• Features
• Installation
• Configuration
• Client Setup
• Data Protection
• Tutorial
• Disclaimer
• Support

Quick Start

• Install MongoDB Lens
• Configure MongoDB Lens
• Set up your MCP Client (e.g. Claude Desktop)
• Explore your MongoDB databases with natural language queries

Features

• Tools
• Resources
• Prompts
• Other

Tools

• aggregate-data: Execute aggregation pipelines
• analyze-query-patterns: Analyze live queries and suggest optimizations
• analyze-schema: Automatically infer collection schemas
• bulk-operations: Perform multiple operations efficiently (requires confirmation for destructive operations)
• collation-query: Find documents with language-specific collation rules
• compare-schemas: Compare schemas between two collections
• connect-mongodb: Connect to a different MongoDB URI
• connect-original: Connect back to the original MongoDB URI used at startup
• count-documents: Count documents matching specified criteria
• create-collection: Create new collections with custom options
• create-database: Create a new database with option to switch to it
• create-index: Create new indexes for performance optimization
• create-timeseries: Create time series collections for temporal data
• create-user: Create new database users with specific roles
• current-database: Show the current database context
• delete-document: Delete documents matching specified criteria (requires confirmation)
• distinct-values: Extract unique values for any field
• drop-collection: Remove collections from the database (requires confirmation)
• drop-database: Drop a database (requires confirmation)
• drop-index: Remove indexes from collections (requires confirmation)
• drop-user: Remove database users (requires confirmation)
• explain-query: Analyze query execution plans
• export-data: Export query results in JSON or CSV format
• find-documents: Run queries with filters, projections, and sorting
• generate-schema-validator: Generate JSON Schema validators
• geo-query: Perform geospatial queries with various operators
• get-stats: Retrieve database or collection statistics
• gridfs-operation: Manage large files with GridFS buckets
• list-collections: Explore collections in the current database
• list-databases: View all accessible databases
• map-reduce: Run MapReduce operations for complex data processing
• modify-document: Insert or update specific documents
• rename-collection: Rename existing collections (requires confirmation when dropping targets)
• shard-status: View sharding configuration for databases and collections
• text-search: Perform full-text search across text-indexed fields
• transaction: Execute multiple operations in a single ACID transaction
• use-database: Switch to a specific database context
• validate-collection: Check for data inconsistencies
• watch-changes: Monitor real-time changes to collections

Resources

• collection-indexes: Index information for a collection
• collection-schema: Schema information for a collection
• collection-stats: Performance statistics for a collection
• collection-validation: Validation rules for a collection
• collections: List of collections in the current database
• database-triggers: Database change streams and event triggers configuration
• database-users: Database users and roles in the current database
• databases: List of all accessible databases
• performance-metrics: Real-time performance metrics and profiling data
• replica-status: Replica set status and configuration
• server-status: Server status information
• stored-functions: Stored JavaScript functions in the current database

Prompts

• aggregation-builder: Step-by-step creation of aggregation pipelines
• backup-strategy: Customized backup and recovery recommendations
• data-modeling: Expert advice on MongoDB schema design for specific use cases
• database-health-check: Comprehensive database health assessment and recommendations
• index-recommendation: Get personalized index suggestions based on query patterns
• inspector-guide: Get help using MongoDB Lens with MCP Inspector
• migration-guide: Step-by-step MongoDB version migration plans
• mongo-shell: Generate MongoDB shell commands with explanations
• multi-tenant-design: Design MongoDB multi-tenant database architecture
• query-builder: Interactive guidance for constructing MongoDB queries
• query-optimizer: Optimization recommendations for slow queries
• schema-analysis: Detailed collection schema analysis with recommendations
• schema-versioning: Manage schema evolution in MongoDB applications
• security-audit: Database security analysis and improvement recommendations
• sql-to-mongodb: Convert SQL queries to MongoDB aggregation pipelines

Other Features

• Other Features: Overview
• Other Features: New Database Metadata
• Other Features: MongoDB Version Compatibility

Other Features: Overview

MongoDB Lens includes several additional features:

• Sanitized Inputs: Security enhancements for query processing
• Configuration File: Custom configuration via ~/.mongodb-lens.json
• Connection Resilience: Automatic reconnection with exponential backoff
• JSONRPC Error Handling: Comprehensive error handling with proper error codes
• Memory Management: Automatic memory monitoring and cleanup for large operations
• Smart Caching: Enhanced caching for schemas, collection lists, and server status

Other Features: New Database Metadata

When MongoDB Lens creates a new database via the create-database tool, it automatically adds a metadata collection containing a single document. This serves several purposes:

• MongoDB only persists databases containing at least one collection
• Records database creation details (timestamp, tool version, user)
• Captures environment information for diagnostics

js
{
    "_id" : ObjectId("67d5284463788ec38aecee14"),
    "created" : {
        "timestamp" : ISODate("2025-03-15T07:12:04.705Z"),
        "tool" : "MongoDB Lens v5.0.7",
        "user" : "anonymous"
    },
    "mongodb" : {
        "version" : "3.6.23",
        "connectionInfo" : {
            "host" : "unknown",
            "readPreference" : "primary"
        }
    },
    "database" : {
        "name" : "example_database",
        "description" : "Created via MongoDB Lens"
    },
    "system" : {
        "hostname" : "unknown",
        "platform" : "darwin",
        "nodeVersion" : "v22.14.0"
    },
    "lens" : {
        "version" : "5.0.7",
        "startTimestamp" : ISODate("2025-03-15T07:10:06.084Z")
    }
}

You can safely remove this collection once you've added your own collections to the new database.

Other Features: MongoDB Version Compatibility

MongoDB Lens implements a backward compatibility layer to work reliably with both older MongoDB deployments and latest versions, providing consistent behavior without requiring version-specific configuration.

Installation

MongoDB Lens can be installed and run in several ways:

• NPX (Easiest)
• Docker Hub
• Node.js from Source
• Docker from Source
• Installation Verification

Installation: NPX

[!NOTE]
NPX requires Node.js installed and running on your system (suggestion: use Volta).

The easiest way to run MongoDB Lens is using npx:

console
# Ensure Node.js is installed
node --version # Ideally >= v22.x but MongoDB Lens is >= v18.x compatible

# Using default connection string mongodb://localhost:27017
npx -y mongodb-lens

# Using custom connection string
npx -y mongodb-lens mongodb://your-connection-string

[!TIP]
If you encounter permissions errors with npx try running npx clear-npx-cache prior to running npx -y mongodb-lens (this clears the cache and re-downloads the package).

Installation: Docker Hub

[!NOTE]
Docker Hub requires Docker installed and running on your system.

Run MongoDB Lens via Docker Hub:

console
# Using default connection string mongodb://localhost:27017
docker run --rm -i --network=host furey/mongodb-lens

# Using custom connection string
docker run --rm -i --network=host furey/mongodb-lens mongodb://your-connection-string

# Using "--pull" to keep the Docker image up-to-date
docker run --rm -i --network=host --pull=always furey/mongodb-lens

Installation: Node.js from Source

[!NOTE]
Node.js from source requires Node.js installed and running on your system (suggestion: use Volta).

1. Clone the MongoDB Lens repository:
   

   console
   git clone https://github.com/furey/mongodb-lens.git
   

2. Navigate to the cloned repository directory:
   

   console
   cd /path/to/mongodb-lens
   

3. Ensure Node.js is installed:
   

   console
   node --version # Ideally >= v22.x but MongoDB Lens is >= v18.x compatible
   

4. Install Node.js dependencies:
   

   console
   npm ci
   

5. Start the server:
   

   console
   # Using default connection string mongodb://localhost:27017
   node mongodb-lens.js
   
   # Using custom connection string
   node mongodb-lens.js mongodb://your-connection-string
   

Installation: Docker from Source

[!NOTE]
Docker from source requires Docker installed and running on your system.

1. Clone the MongoDB Lens repository:
   

   console
   git clone https://github.com/furey/mongodb-lens.git
   

2. Navigate to the cloned repository directory:
   

   console
   cd /path/to/mongodb-lens
   

3. Build the Docker image:
   

   console
   docker build -t mongodb-lens .
   

4. Run the container:
   

   console
   # Using default connection string mongodb://localhost:27017
   docker run --rm -i --network=host mongodb-lens
   
   # Using custom connection string
   docker run --rm -i --network=host mongodb-lens mongodb://your-connection-string
   

Installation Verification

To verify the installation, paste and run the following jsonrpc message into the server's stdio:

json
{"method":"resources/read","params":{"uri":"mongodb://databases"},"jsonrpc":"2.0","id":1}

The server should respond with a list of databases in your MongoDB instance, for example:

json
{"result":{"contents":[{"uri":"mongodb://databases","text":"Databases (12):\n- admin (180.00 KB)\n- config (108.00 KB)\n- local (40.00 KB)\n- sample_airbnb (51.88 MB)\n- sample_analytics (9.46 MB)\n- sample_geospatial (980.00 KB)\n- sample_guides (40.00 KB)\n- sample_mflix (108.90 MB)\n- sample_restaurants (7.73 MB)\n- sample_supplies (968.00 KB)\n- sample_training (40.85 MB)\n- sample_weatherdata (2.69 MB)"}]},"jsonrpc":"2.0","id":1}

MongoDB Lens is now installed and ready to accept MCP requests.

Configuration

• MongoDB Connection String
• Verbose Logging
• Config File

Configuration: MongoDB Connection String

The server accepts a MongoDB connection string as its only argument.

Example NPX usage:

console
npx -y mongodb-lens mongodb://your-connection-string

MongoDB connection strings have the following format:

txt
mongodb://[username:password@]host[:port][/database][?options]

Example connection strings:

• Local connection:
  mongodb://localhost:27017
• Connection to mydatabase with credentials from admin database:
  mongodb://username:password@hostname:27017/mydatabase?authSource=admin
• Connection to mydatabase with various other options:
  mongodb://hostname:27017/mydatabase?retryWrites=true&w=majority

If no connection string is provided, the server will attempt to connect via local connection.

Configuration: Verbose Logging

With verbose logging enabled, the server will output additional information to the console.

To enable verbose logging, set environment variable LOG_LEVEL to verbose.

Example NPX usage:

console
LOG_LEVEL=verbose npx -y mongodb-lens mongodb://your-connection-string

Example Docker Hub usage:

console
docker run --rm -i --network=host -e LOG_LEVEL='verbose' furey/mongodb-lens mongodb://your-connection-string

Configuration: Config File

MongoDB Lens can also be configured via JSON config file: ~/.mongodb-lens.json

Alternatively, set environment variable CONFIG_PATH to the path of your custom config file.

Example NPX usage:

console
CONFIG_PATH='/path/to/config.json' npx -y mongodb-lens

Example Docker Hub usage:

console
docker run --rm -i --network=host -v /path/to/config.json:/root/.mongodb-lens.json furey/mongodb-lens

Example configuration file contents:

json
{
  "mongoUri": "mongodb://username:password@hostname:27017/mydatabase?authSource=admin",
  "connectionOptions": {
    "maxPoolSize": 20,
    "connectTimeoutMS": 30000
  }
}

Client Setup

• Claude Desktop
• MCP Inspector
• Other MCP Clients

Client Setup: Claude Desktop

To use MongoDB Lens with Claude Desktop:

1. Install Claude Desktop
2. Open claude_desktop_config.json (create it if it doesn't exist):
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
3. Add the MongoDB Lens server configuration as per configuration options
4. Restart Claude Desktop
5. Start a conversation with Claude about your MongoDB data

Claude Desktop Configuration Options

• Option 1: NPX (Recommended)
• Option 2: Docker Hub Image
• Option 3: Local Node.js Installation
• Option 4: Local Docker Image

For each option:

• Replace mongodb://your-connection-string with your MongoDB connection string or omit it to use the default mongodb://localhost:27017.
• For verbose logging, set LOG_LEVEL to verbose, otherwise set to info (or omit entirely).
• To use a custom config file, see Configuration: Config File and adapt option accordingly.

Option 1: NPX (Recommended)

json
{
  "mcpServers": {
    "mongodb-lens": {
      "command": "/path/to/npx",
      "args": [
        "-y",
        "mongodb-lens",
        "mongodb://your-connection-string"
      ],
      "env": {
        "LOG_LEVEL": "[verbose|info]"
      }
    }
  }
}

Option 2: Docker Hub Image

json
{
  "mcpServers": {
    "mongodb-lens": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "--network=host",
        "--pull=always",
        "-e",
        "LOG_LEVEL=[verbose|info]",
        "furey/mongodb-lens",
        "mongodb://your-connection-string"
      ]
    }
  }
}

Option 3: Local Node.js Installation

json
{
  "mcpServers": {
    "mongodb-lens": {
      "command": "/path/to/node",
      "args": [
        "/path/to/mongodb-lens.js",
        "mongodb://your-connection-string"
      ],
      "env": {
        "LOG_LEVEL": "[verbose|info]"
      }
    }
  }
}

Option 4: Local Docker Image

json
{
  "mcpServers": {
    "mongodb-lens": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "--network=host",
        "-e",
        "LOG_LEVEL=[verbose|info]",
        "mongodb-lens",
        "mongodb://your-connection-string"
      ]
    }
  }
}

Client Setup: MCP Inspector

MCP Inspector is a tool designed for testing and debugging MCP servers.

[!NOTE]
MCP Inspector starts a proxy server on port 3000 and web client on port 5173.

Example NPX usage:

1. Run MCP Inspector:
   

   console
   # Using default connection string mongodb://localhost:27017
   npx -y @modelcontextprotocol/inspector npx -y mongodb-lens
   
   # Using custom connection string
   npx -y @modelcontextprotocol/inspector npx -y mongodb-lens mongodb://your-connection-string
   
   # Using verbose logging
   npx -y @modelcontextprotocol/inspector -e LOG_LEVEL=verbose npx -y mongodb-lens
   
   # Using custom ports
   SERVER_PORT=1234 CLIENT_PORT=5678 npx -y @modelcontextprotocol/inspector npx -y mongodb-lens
   

2. Open MCP Inspector: http://localhost:5173

MCP Inspector should support the full range of MongoDB Lens capabilities, including autocompletion for collection names and query fields.

For more, see: MCP Inspector

Client Setup: Other MCP Clients

MongoDB Lens should be usable with any MCP-compatible client.

For more, see: MCP Documentation: Example Clients

Data Protection

To protect your data while using MongoDB Lens, consider the following:

• Read-Only User Accounts
• Working with Database Backups
• Confirmation for Destructive Operations

Data Protection: Read-Only User Accounts

When connecting MongoDB Lens to your database, the permissions granted to the user in the MongoDB connection string dictate what actions can be performed. When the use case fits, a read-only user can prevent unintended writes or deletes, ensuring MongoDB Lens can query data but not alter it.

To set this up, create a user with the read role scoped to the database(s) you're targeting. In MongoDB shell, you'd run something like:

js
use admin

db.createUser({
  user: 'readonly',
  pwd: 'eXaMpLePaSsWoRd',
  roles: [{ role: 'read', db: 'mydatabase' }]
})

Then, apply those credentials to your MongoDB connection string:

text
mongodb://readonly:eXaMpLePaSsWoRd@localhost:27017/mydatabase

Using read-only credentials is a simple yet effective way to enforce security boundaries, especially when you're poking around schemas or running ad-hoc queries.

Data Protection: Working with Database Backups

When working with MongoDB Lens, consider connecting to a backup copy of your data hosted on a separate MongoDB instance.

Start by generating the backup with mongodump. Next, spin up a fresh MongoDB instance (e.g. on a different port like 27018) and restore the backup there using mongorestore. Once it's running, point MongoDB Lens to the backup instance's connection string (e.g. mongodb://localhost:27018/mydatabase).

This approach gives you a sandbox to test complex or destructive operations against without risking accidental corruption of your live data.

Data Protection: Confirmation for Destructive Operations

MongoDB Lens implements a token-based confirmation system for potentially destructive operations, requiring a two-step process to execute tools that may otherwise result in unchecked data loss:

1. First tool invocation: Returns a 4-digit confirmation token that expires after 5 minutes
2. Second tool invocation: Executes the operation if provided with the valid token

For an example of the confirmation process, see: Working with Confirmation Protection.

Tools that require confirmation include:

• bulk-operations: When including delete operations
• delete-document: Delete one or multiple documents
• drop-collection: Delete a collection and all its documents
• drop-database: Permanently delete a database
• drop-index: Remove an index (potential performance impact)
• drop-user: Remove a database user
• rename-collection: When the target collection exists and will be dropped

This protection mechanism aims to prevent accidental data loss from typos and unintended commands. It's a safety net ensuring you're aware of the consequences before proceeding with potentially harmful actions.

[!NOTE]
If you're working in a controlled environment where data loss is acceptable, you can configure MongoDB Lens to bypass confirmation and perform destructive operations immediately.

Bypassing Confirmation for Destructive Operations

You might want to bypass the token confirmation system.

Set the environment variable DISABLE_DESTRUCTIVE_OPERATION_TOKENS to true to execute destructive operations immediately without confirmation:

console
# Using NPX
DISABLE_DESTRUCTIVE_OPERATION_TOKENS=true npx -y mongodb-lens

# Using Docker
docker run --rm -i --network=host -e DISABLE_DESTRUCTIVE_OPERATION_TOKENS='true' furey/mongodb-lens

[!WARNING]
Disabling confirmation tokens removes an important safety mechanism. It's strongly recommended to only use this option in controlled environments where data loss is acceptable, such as development or testing. Disable at your own risk.

Tutorial

This following tutorial guides you through setting up a MongoDB container with sample data, then using MongoDB Lens to interact with it through natural language queries:

1. Start Sample Data Container
2. Import Sample Data
3. Connect MongoDB Lens
4. Example Queries
5. Working With Confirmation Protection

Tutorial: 1. Start Sample Data Container

[!NOTE]
This tutorial assumes you have Docker installed and running on your system.

[!IMPORTANT]
If Docker is already running a container on port 27017, stop it before proceeding.

1. Initialise the sample data container:
   

   console
   docker run --name mongodb-sampledata -d -p 27017:27017 mongo:6
   

2. Verify the container is running without issue:
   

   console
   docker ps | grep mongodb-sampledata
   

Tutorial: 2. Import Sample Data

MongoDB provides several sample datasets which we'll use to explore MongoDB Lens.

1. Download the sample datasets:

   console
   curl -LO https://atlas-education.s3.amazonaws.com/sampledata.archive
   

2. Copy the sample datasets into your sample data container:
   

   console
   docker cp sampledata.archive mongodb-sampledata:/tmp/
   

3. Import the sample datasets into MongoDB:
   

   console
   docker exec -it mongodb-sampledata mongorestore --archive=/tmp/sampledata.archive
   

This will import several databases:

• sample_airbnb: Airbnb listings and reviews
• sample_analytics: Customer and account data
• sample_geospatial: Geographic data
• sample_mflix: Movie data
• sample_restaurants: Restaurant data
• sample_supplies: Supply chain data
• sample_training: Training data for various applications
• sample_weatherdata: Weather measurements

Tutorial: 3. Connect MongoDB Lens

Install MongoDB Lens as per the Quick Start instructions.

Set your MCP Client to connect to MongoDB Lens via: mongodb://localhost:27017

[!TIP]
Omitting the connection string from your MCP Client configuration will default the connection string to mongodb://localhost:27017.

Example Claude Desktop configuration:

json
{
  "mcpServers": {
    "mongodb-lens": {
      "command": "/path/to/npx",
      "args": [
        "-y",
        "mongodb-lens"
      ]
    }
  }
}

Tutorial: 4. Example Queries

With your MCP Client running and connected to MongoDB Lens, try the following example queries:

• Example Queries: Basic Database Operations
• Example Queries: Collection Management
• Example Queries: User Management
• Example Queries: Querying Data
• Example Queries: Schema Analysis
• Example Queries: Data Modification
• Example Queries: Performance & Index Management
• Example Queries: Geospatial & Special Operations
• Example Queries: Export, Administrative & Other Features
• Example Queries: Connection Management

Example Queries: Basic Database Operations

• "List all available databases"
  ^{➥ Uses list-databases tool}
• "What database am I currently using?"
  ^{➥ Uses current-database tool}
• "Switch to the sample_mflix database"
  ^{➥ Uses use-database tool}
• "Create a new database called test_db"
  ^{➥ Uses create-database tool}
• "Create another database called analytics_db and switch to it"
  ^{➥ Uses create-database tool with switch=true}
• "Drop the test_db database"
  ^{➥ Uses drop-database tool (with confirmation)}

Example Queries: Collection Management

• "What collections are in the current database?"
  ^{➥ Uses list-collections tool}
• "Create a new collection named user_logs"
  ^{➥ Uses create-collection tool}
• "Drop the user_logs collection"
  ^{➥ Uses drop-collection tool (with confirmation)}
• "Rename the user_logs collection to system_logs"
  ^{➥ Uses rename-collection tool}
• "Check the data consistency in the movies collection"
  ^{➥ Uses validate-collection tool}

Example Queries: User Management

• "Create a read-only user for analytics"
  ^{➥ Uses create-user tool}
• "Drop the inactive_user account"
  ^{➥ Uses drop-user tool (with confirmation)}

Example Queries: Querying Data

• "Count all documents in the movies collection"
  ^{➥ Uses count-documents tool}
• "Find the top 5 movies with the highest IMDB rating"
  ^{➥ Uses find-documents tool}
• "Show me aggregate data for movies grouped by decade"
  ^{➥ Uses aggregate-data tool}
• "List all unique countries where movies were produced"
  ^{➥ Uses distinct-values tool}
• "Search for movies containing 'godfather' in their title"
  ^{➥ Uses text-search tool}
• "Find German users with last name 'müller' using proper collation"
  ^{➥ Uses collation-query tool}

Example Queries: Schema Analysis

• "What's the schema structure of the movies collection?"
  ^{➥ Uses analyze-schema tool}
• "Compare the schema between users and comments collections"
  ^{➥ Uses compare-schemas tool}
• "Generate a JSON schema validator for the movies collection"
  ^{➥ Uses generate-schema-validator tool}
• "Analyze common query patterns for the movies collection"
  ^{➥ Uses analyze-query-patterns tool}

Example Queries: Data Modification

• "Insert a new movie document"
  ^{➥ Uses modify-document tool (insert operation)}
• "Update all movies from 1994 to add a 'classic' flag"
  ^{➥ Uses modify-document tool (update operation)}
• "Delete all movies with zero ratings"
  ^{➥ Uses delete-document tool (with confirmation)}
• "Run these bulk operations on the movies collection"
  ^{➥ Uses bulk-operations tool}

Example Queries: Performance & Index Management

• "Create an index on the title field in the movies collection"
  ^{➥ Uses create-index tool}
• "Drop the unused ratings_idx index"
  ^{➥ Uses drop-index tool (with confirmation)}
• "Explain the execution plan for finding movies from 1995"
  ^{➥ Uses explain-query tool}
• "Get statistics for the current database"
  ^{➥ Uses get-stats tool (database target)}
• "Show collection stats for the movies collection"
  ^{➥ Uses get-stats tool (collection target)}

Example Queries: Geospatial & Special Operations

• "Switch to sample_geospatial database, then find all shipwrecks within 10km of coordinates [-80.12, 26.46]"
  ^{➥ Uses geo-query tool}
• "Switch to sample_mflix database, then run this Map-Reduce to calculate movie counts by year with map 'function () { emit(this.year, 1) }' and reduce 'function (key, values) { return Array.sum(values) }'"
  ^{➥ Uses map-reduce tool}
• "Switch to sample_analytics database, then execute a transaction to move funds between accounts"
  ^{➥ Uses transaction tool}
• "Create a time series collection for sensor readings"
  ^{➥ Uses create-timeseries tool}
• "Watch for changes in the users collection for 30 seconds"
  ^{➥ Uses watch-changes tool}
• "List all files in the images GridFS bucket"
  ^{➥ Uses gridfs-operation tool (list operation)}

Example Queries: Export, Administrative & Other Features

• "Switch to sample_mflix database, then export the top 20 movies based on 'tomatoes.critic.rating' as a CSV with title, year and rating fields, output as raw csv text in a single code block"
  ^{➥ Uses export-data tool}
• "Switch to sample_analytics database, then check its sharding status"
  ^{➥ Uses shard-status tool}
• "Switch to sample_weatherdata database, and generate an interactive report on its current state"
  ^{➥ Uses numerous tools}

Example Queries: Connection Management

• "Connect to a different MongoDB server at mongodb://localhost:27018"
  ^{➥ Uses connect-mongodb tool}
• "Connect to MongoDB Atlas instance at mongodb+srv://username:password@cluster.mongodb.net/mydb"
  ^{➥ Uses connect-mongodb tool}
• "Connect back to the original MongoDB server"
  ^{➥ Uses connect-original tool}
• "Connect to a MongoDB replica set without validating the connection"
  ^{➥ Uses connect-mongodb tool with validateConnection=false}

Tutorial: 5. Working With Confirmation Protection

MongoDB Lens includes a safety mechanism for potentially destructive operations. Here's how it works in practice:

1. Request to drop a collection:
   

   "Drop the collection named test_collection"
   

2. MongoDB Lens responds with a warning and confirmation token:
   

   ⚠️ DESTRUCTIVE OPERATION WARNING ⚠️
   
   You've requested to drop the collection 'test_collection'.
   
   This operation is irreversible and will permanently delete all data in this collection.
   
   To confirm, you must type the 4-digit confirmation code EXACTLY as shown below:
   
   Confirmation code: 9876
   
   This code will expire in 5 minutes for security purposes.
   

3. Confirm the operation by submitting the confirmation token:
   

   "9876"
   

4. MongoDB Lens executes the operation:
   

   Collection 'test_collection' has been permanently deleted.
   

This two-step process prevents accidental data loss by requiring explicit confirmation.

[!NOTE]
If you're working in a controlled environment where data loss is acceptable, you can configure MongoDB Lens to bypass confirmation and perform destructive operations immediately.

Disclaimer

MongoDB Lens:

• is licensed under the MIT License.
• is not affiliated with or endorsed by MongoDB, Inc.
• is written with the assistance of AI and may contain errors.
• is intended for educational and experimental purposes only.
• is provided as-is with no warranty—please use at your own risk.

Support

If you've found MongoDB Lens helpful consider supporting my work through:

Buy Me a Coffee | GitHub Sponsorship

Contributions help me continue developing and improving this tool, allowing me to dedicate more time to add new features and ensuring it remains a valuable resource for the community.