For AI agents: a documentation index is available at https://www.mongodb.com/docs/llms.txt — markdown versions of all pages are available by appending .md to any URL path.

MongoDB MCP Server Usage Examples

This page demonstrates how to use the MongoDB MCP Server through practical examples with natural language prompts. Each example shows the prompt you can enter in your AI client and an example response you might receive from the large language model (LLM).

You can use the examples on this page as starting points for your own interactions with the MongoDB MCP Server.

Note

The exact output you receive will vary depending on your data, AI client, and the LLM you're using. Private information such as organization IDs, project IDs, and passwords are redacted in these examples.

Prerequisites

Before running these examples, ensure that you have the following:

Atlas Administration

The following examples demonstrate how to manage your Atlas infrastructure by using the MongoDB MCP Server.

View Organizations and Projects

Get an overview of your Atlas account structure and available resources.

Show my Atlas organizations and projects

Create an Atlas Cluster

Set up a new Atlas project with a cluster and all necessary access configurations in a single workflow.

Create a new Atlas project named myNewProject and create afree cluster named myNewCluster in the new project, addaccess list for my current IP, create a database user namedmyNewUser with read and write access to the new cluster, andreturn the connection string for the new cluster

List Supported Atlas Regions

To look up the region codes and human-readable locations that Atlas supports for a cloud provider use atlas-get-regions. This is useful for resolving a natural-language location, such as "Iowa" or "Northern Europe", to the exact region code that tools like atlas-create-cluster and atlas-upgrade-cluster expect.

What Atlas regions are available on GCP?

Create a Dedicated Atlas Cluster

Use atlas-create-cluster to create a dedicated Atlas cluster with M10–M80 instance sizes. To create a free-tier cluster instead, use atlas-create-free-cluster.

The tool requires projectId, clusterName, provider, and regions.

Create a dedicated Atlas cluster named myDedicatedClusterin project myProject on AWS in US East

Non-Default Options

To specify a non-default cluster type, instance size, or backup settings, include those options in your prompt:

Create a dedicated Atlas cluster named myShardedClusterin project myProject on AWS in US East with cluster typeSHARDED, instance size M30, and continuous backups enabled

Multiple Regions

To distribute a cluster across multiple regions, name up to three regions in your prompt. The tool treats the regions as an ordered list: the first region has the highest priority and holds the primary node. The tool distributes electable nodes as three nodes in one region, two and one node across two regions, or two, two, and one node across three regions.

Create a dedicated Atlas cluster named myMultiRegionClusterin project myProject on AWS across US East, Ohio, and Ireland

Encryption at Rest with a Customer-Managed Key

To encrypt a cluster with a customer-managed key, name the key provider in your prompt. The provider must already have a valid Encryption at Rest configuration in the project. The tool selects an existing configuration, it can't create or modify one. To learn how to configure a key provider, see Encryption at Rest using Customer Key Management.

If you don't name a provider, the tool uses the customer-managed key configured for the cluster's cloud provider when a valid configuration exists. Otherwise, the cluster uses the default Atlas Encryption at Rest.

Create a dedicated Atlas cluster named myEncryptedClusterin project myProject on AWS in US East, encrypted with ourAWS customer-managed key

Upgrade an Atlas Cluster

The atlas-upgrade-cluster tool supports the following upgrade paths:

  • Free to Flex

  • Free to M10

  • Flex to M10

The tool also scales a dedicated cluster to a different M10–M80 instance size and updates its compute autoscaling settings.

Connected to a Cluster

If you configured a connection string in your MCP settings, or you connected using the atlas-connect-cluster tool, the MCP Server infers the cluster name, project, and current tier from your connection.

Upgrade my current Atlas cluster to Flex

Not Connected to a Cluster

If you do not have an active cluster connection, provide the cluster name and project in your prompt. You can optionally specify the target tier, provider, and region.

Upgrade my Atlas cluster named myFlexClusterin project myProject to M10 on AWS in US East

Scaling a Dedicated Cluster

To scale a dedicated cluster, specify at least one of the following options:

  • The new instance size

  • Whether to enable compute autoscaling

  • The minimum or maximum instance size for autoscaling

Scale my Atlas cluster named myDedicatedClusterin project myProject to M30 and let it autoscalebetween M20 and M50

Pause or Resume an Atlas Cluster

The atlas-pause-resume-cluster tool pauses or resumes a dedicated (M10+) Atlas cluster. Free and Flex clusters cannot be paused.

The tool requires the following parameters:

  • projectId

  • clusterName

  • action. Set action to PAUSE or RESUME.

Pause a Cluster

When you pause a cluster, it becomes unavailable for connections and does not incur compute costs. If the cluster is your current active connection, the MCP Server automatically disconnects from it after the cluster is paused.

Pause cluster test-cluster-1 from project XXX

Resume a Cluster

When you resume a cluster, it does not become immediately available for connections. Use atlas-inspect-cluster to poll the cluster state until it reaches IDLE.

Resume cluster test-cluster-1 from project XXX

Data Exploration

The following examples demonstrate how you can use the MongoDB MCP Server to explore and understand your MongoDB data.

List Available Databases

Discover what databases are available in your cluster and get insights about their contents.

Show my Atlas databases

Explore Collections

Dive deeper into a specific database to understand its structure and collections.

Show the collections in the Atlas sample_mflix database

Data Analysis

Analyize your MongoDB data to identify patterns and trends.

Summarize the Atlas movies collection by thrillers

Database Operations

The following examples demonstrate how to use the MongoDB MCP Server to perform common database operations.

Insert New Documents

Add new data to your collections.

Add a document to the movies_copy collection

Create New Collections

Create and customize new collections.

Create a new collection to store movie purchases data thatincludes geospatial and timeseries fields

Copy an Existing Collection

Create a backup or duplicate of an existing collection.

Make a copy of the movies collection named movies_copy

Data Export and Sharing

The following example demonstrates how to export query results for sharing or further processing in external tools.

Generate comprehensive reports and export them for use in other applications or for sharing with team members.

The exported results are saved to a file on the computer that runs the MCP Server. You can also access the exported data through the exported-data resource using the AI client application.

The export file is stored in the file system path specified by the exportPath configuration option. The export file is eligible for deletion after the time period specified by the exportTimeout configuration option. For additional details, see Export Data from MongoDB MCP Server.

Summarize and export the contents of the movies collection.

Local Atlas Deployments

You can use the Local MCP with local Atlas deployments. To do so, you must install Docker. For an introduction to local Atlas deployments, see Create a Local Atlas Deployment.

The following examples show how to use the MongoDB MCP Server to interact with and create local Atlas deployments.

List Local Atlas Deployments

The following example lists local Atlas deployments.

List all local Atlas deployments.

Connect to Local Atlas Deployment and List Databases

The following example connects to a local Atlas deployment named local7356 and lists the databases.

Connect to my local Atlas deployment local7356 and list thedatabases.

Create New Local Atlas Deployment with Database and Collection

The following example creates a new local Atlas deployment with a database and an example collection. To run the example, you must disable read only mode.

Create a new local Atlas deployment, connect to it, create acollection with sample pizza orders data, and then retrieve thesample data.

Delete Local Atlas Deployment

The following example deletes a local Atlas deployment named local5528. To run the example, you must disable read only mode.

Delete my local Atlas deployment local5528.

After the atlas-local-delete-deployment tool deletes the local Atlas deployment, the AI client might respond that the deployment has already been deleted. This is because the atlas-local-delete-deployment tool ran successfully and the local Atlas deployment no longer exists.

Performance Optimization

The following examples demonstrate how you can use the MongoDB MCP Server to identify and resolve performance issues. The actual output for your prompts will depend on whether your cluster is experiencing performance issues, so your output might not match the examples exactly.

These examples use the atlas-get-performance-advisor tool to retrieve recommendations from the Performance Advisor.

Analyze Slow Queries

Identify and analyze slow-performing queries to understand performance bottlenecks.

Note

When performing slow query analysis, the MongoDB MCP Server retrieves a sample of slow queries, capped at 50 queries. The sample includes up to 50 most recent slow queries that match any specified conditions in your prompt to ensure optimal performance and response times.

Query for all slow queries in the cluster:

Show me slow queries in my cluster

Query for general performance issues:

How is my cluster performance?

Get Index Recommendations

Use the Performance Advisor to suggest indexes to create or drop to improve performance.

What indexes should I create or drop to improve performance?

Optimize Your Schema

Get recommendations for improving your database schema design and structure.

Suggest schema improvements for my database

Vector Search

The following examples demonstrate how to use the MongoDB MCP Server to work with MongoDB Vector Search. To learn more about how the MCP Server works with vector search, see Vector Search Support.

Note

To use automatic embedding generation, you must configure the MCP Server with a Voyage AI API key.

Create and Manage Vector Search Indexes

Create, drop, and list vector search indexes for your collections.

Create a vector search index on the sample_db.products collectionfor the embeddings field with 1024 dimensionsusing dot product similarity
Show me all vector search indexes on the sample_db.products collection
Drop the vector search index from the sample_db.products collection

Insert Documents with Automatic Embeddings

When you configure a Voyage AI API key, the MCP Server automatically generates embeddings when inserting documents. You can provide raw text values, and the server generates the embeddings for fields with vector search indexes.

Insert documents into the sample_db database and products collection.Use the voyage-3-large embedding model to generatevector embeddings from descriptions for the `embeddings` field.
1. name: "Headphones", description: "Premium wireless noise-canceling headphones with 30-hour battery"2. name: "Earbuds", description: "True wireless earbuds with active noise cancellation"3. name: "Monitor", description: "24-inch 4K monitor with 1ms response time"4. name: "Keyboard", description: "Mechanical keyboard with customizable RGB lighting"5. name: "Mouse", description: "Wireless ergonomic mouse with programmable buttons"

Run a Vector Search Query

When you configure a Voyage AI API key, the MCP Server automatically generates embeddings when running vector search queries. You can provide the raw text and the server automatically generates embeddings for the query.

Run a vector search query on sample_db.products on the 'embeddings' fieldusing the vector search index to find products that I can use to listen to music.

Lexical Search

The following examples demonstrate how to use the MongoDB MCP Server to work with MongoDB Search. To learn more about how the MCP Server works with lexical search, see Lexical Search Support.

Create and Manage Lexical Search Indexes

Create, drop, and list lexical search indexes for your collections.

Create a lexical search index on the sample_db.products collectionfor the description field
Show me all lexical search indexes on the sample_db.products collection
Drop the lexical search index from the sample_db.products collection

Run a Lexical Search Query

Run lexical search queries with the aggregate tool using the $search aggregation stage.

Run a lexical search query on sample_db.products on the 'description' fieldusing the lexical search index to find products that mention wireless.

Get Lexical Search Result Metadata

Use the aggregate tool with the $searchMeta aggregation stage to return metadata about a lexical search, such as a count of matching documents, instead of the matching documents themselves.

How many products have "wireless" in the description onsample_db.products using the lexical search index

Next Steps

For more information about configuring and using the MongoDB MCP Server:

Learn about all available configuration options and connection methods.

Explore the complete list of tools and their capabilities.

Understand how to securely deploy and use the MCP Server.

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