MongoDB MCP Server Tools

For the complete MongoDB documentation index, see www.mongodb.com/docs/llms.txt

MongoDB MCP Server Tools

The MongoDB MCP Server allows you to interact with MongoDB clusters using natural language queries from AI clients that support MCP. This page describes the MCP Server tools.

Overview

The MongoDB MCP Server provides the following tool categories:

  • Atlas tools, which perform operations on Atlas system resources, like organizations, projects, clusters, database user accounts, and retrieving performance recommendations.

  • Local Atlas deployment tools, which allow you to list, connect to, create, and delete local Atlas deployments.

  • Database tools, which perform operations such as inserting, updating, and deleting documents, and running queries and aggregation pipelines.

  • Assistant tools, which search the MongoDB Assistant knowledge base for official documentation and curated guidance.

Not all Atlas tools and Database tools are available on both the MongoDB Atlas Managed MCP Server and Local MCP for MongoDB MCP Server. The Atlas Managed and Local columns in each of these table indicate if an MCP deployment type supports a given tool.

For detailed examples of how the MongoDB MCP Server uses these tools and sample outputs, see [MongoDB MCP Server Usage Examples.](https://www.mongodb.com/docs/mcp-server/examples.md#std-label-mcp-server-examples

MCP Server Atlas Tools

The Atlas tools are only available if you have set up Atlas API credentials as described in [Get Started with the MongoDB MCP Server.](https://www.mongodb.com/docs/mcp-server/get-started.md#std-label-mcp-get-started

The following table describes the Atlas tools. The Atlas Managed and Local columns indicate whether each tool is available on the MongoDB Atlas Managed MCP Server and Local MCP for MongoDB MCP Server, respectively.

Atlas Tool Name Description Atlas Managed Local
atlas-list-orgs Returns a list of Atlas organizations.
atlas-list-projects Returns a list of Atlas projects.
atlas-create-project Creates a new Atlas project.
atlas-list-clusters Returns list of Atlas clusters.
atlas-inspect-cluster Returns information about a specific Atlas cluster.
atlas-create-free-cluster Creates a free Atlas cluster.
atlas-connect-cluster Connects to an Atlas cluster using the configured service account. If you configured the MCP server without specifying a connection string, this tool creates a temporary database user with a random password to establish the connection. For details, see [Tool Details.](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-tools-considerations
atlas-get-regions Returns the supported Atlas regions for a cloud provider (AWS, AZURE, or GCP), including each region's code and human-readable location. Use this tool to resolve region names or codes before creating or upgrading a cluster.
atlas-upgrade-cluster Supports the following Atlas cluster upgrades: Free to Flex; Free to M10; Flex to M10 You can optionally specify the target region and provider. For a dedicated cluster, you can scale the cluster to a different M10–M80 instance size and update its compute autoscaling settings. 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 Scaling applies to electable and read-only nodes. This tool doesn't scale analytics nodes, multi-shard clusters, or clusters whose regions use different instance sizes.
atlas-create-cluster Creates a dedicated Atlas cluster with M10–M80 instance sizes. Supports replica set and single-shard cluster types in one to three regions. clusterType defaults to REPLICASET. SHARDED clusters are single-shard only and require M30 or higher.; regions accepts up to three Atlas regions in priority order. The first region has the highest priority and holds the primary node.; encryptionAtRestProvider selects a customer-managed key provider for Encryption at Rest. The key provider must already have a valid Encryption at Rest configuration in the project.; Compute autoscaling is enabled by default. Disk autoscaling is always enabled.; The tool returns immediately. Use atlas-inspect-cluster to poll until the cluster state reaches IDLE.
atlas-pause-resume-cluster Pauses or resumes a dedicated (M10+) Atlas cluster. Free and Flex clusters cannot be paused. Pause: Makes the cluster unavailable for connections and stops compute costs. The cluster must be in the IDLE state.; Resume: Restarts a paused cluster. Use atlas-inspect-cluster to poll until the cluster state reaches IDLE.
atlas-inspect-access-list Returns information about the IP (Internet Protocol) and CIDR (Classless Inter-Domain Routing) ranges that can access an Atlas cluster.
atlas-create-access-list Configures the IP (Internet Protocol) and CIDR (Classless Inter-Domain Routing) access list for an Atlas cluster.
atlas-list-db-users Returns a list of Atlas database users.
atlas-create-db-user Creates an Atlas database user.
atlas-list-alerts Returns a list of triggered alerts for an Atlas project. These are alerts Atlas has raised, not the alert configurations that define them. Defaults to OPEN alerts. You can set the status to TRACKING or CLOSED to see those respective alerts.
atlas-get-performance-advisor Returns [Performance Advisor](https://www.mongodb.com/docs/atlas/performance-advisor.md#std-label-performance-advisor recommendations for an Atlas cluster. Supports operations for suggested indexes, drop index suggestions, slow query logs, and schema suggestions. Requires [Project Read Only](https://www.mongodb.com/docs/atlas/reference/user-roles.md#mongodb-authrole-Project-Read-Only access or higher. To learn more, see [Performance Advisor Tool.](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-pa-tool
atlas-load-sample-dataset Loads a sample dataset into an Atlas cluster.
remote-atlas-connect Connects to an Atlas cluster on the Remote MCP Server.
atlas-streams-discover Returns information about an Atlas Stream Processing workspace, processor, public connection, or privatelink networking connection. On the Remote MCP Server, the Atlas Stream Processing tools are exposed through the atlas-streams-* tools.
atlas-streams-build Creates an Atlas Stream Processing workspace, processor, public connection, or privatelink networking connection.
atlas-streams-manage Manages an Atlas Stream Processing workspace, processor, or public connection. The MongoDB MCP Server can: Start, stop, or update a processor.; Update a workspace.; Update a public connection.; Accept or reject a privatelink networking connection.
atlas-streams-teardown Deletes an Atlas Stream Processing workspace, processor, public connection, or privatelink networking connection.

MCP Server Local Atlas Tools

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 table describes the local Atlas tools. These tools are only available to the Local MCP for MongoDB MCP Server.

MCP Server Local Atlas Tool Name Description
atlas-local-list-deployments Lists local Atlas deployments.
atlas-local-create-deployment Creates a local Atlas deployment. To run this tool, you must disable [read only mode.](https://www.mongodb.com/docs/mcp-server/local-mcp/configuration/enable-or-disable-features.md#std-label-mcp-server-configuration-read-only-mode
atlas-local-connect-deployment Connects to a local Atlas deployment.
atlas-local-delete-deployment Deletes a local Atlas deployment. To run this tool, you must disable [read only mode.](https://www.mongodb.com/docs/mcp-server/local-mcp/configuration/enable-or-disable-features.md#std-label-mcp-server-configuration-read-only-mode

For examples that run the local Atlas tools, see [Local Atlas Deployments.](https://www.mongodb.com/docs/mcp-server/examples.md#std-label-mcp-server-examples-local-atlas-deployments

MCP Server Database Tools

The following table describes the database tools. The Atlas Managed and Local columns indicate whether each tool is available on the MongoDB Atlas Managed MCP Server and Local MCP for MongoDB MCP Server, respectively.

Database Tool Name Description Atlas Managed Local
connect Connects to a MongoDB cluster.
disconnect Closes a MongoDB connection and revokes its connection ID.
list-connections Returns a list of the active MongoDB connections and their connection IDs. Use this tool to find the connection ID of an established connection.
find Runs a MongoDB database query.
aggregate Runs a MongoDB aggregation pipeline.
aggregate-db Runs a MongoDB aggregation pipeline at the database level.
count Returns the number of documents in a collection.
explain Returns statistics describing the execution of the winning plan chosen by the query optimizer for the evaluated method.
insert-many Adds documents to a collection.
create-index Creates an index on a collection. This tool supports creating [vector search indexes](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-vector-search-tools and [lexical search indexes.](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-lexical-search-tools
drop-index Drop an index for the provided database and collection. This tool supports dropping [vector search indexes](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-vector-search-tools and [lexical search indexes.](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-lexical-search-tools
update-many Modifies multiple documents in a collection.
rename-collection Changes the name of a collection.
create-collection Creates a new collection.
delete-many Removes documents from a collection.
drop-collection Deletes a collection from a database.
drop-database Deletes a database.
list-databases Returns a list of all databases available through the current connection.
list-collections Returns a list of collections in a database.
collection-indexes Returns information about collection indexes, including [vector search indexes](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-vector-search-tools and [lexical search indexes.](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-lexical-search-tools
collection-schema Returns collection [schema](https://www.mongodb.com/docs/manual/core/schema-validation.md#std-label-schema-validation-overview information.
collection-storage-size Returns collection size in megabytes.
db-stats Returns database statistics.
export Saves the results of a query or aggregation pipeline in JSON format to a file on the computer that runs the MCP Server. The results are also available through the exported-data resource in the AI client application.
mongodb-logs Returns the most recent logged mongod events.

MCP Server Assistant Tools

The assistant tools give your AI client access to the MongoDB Assistant knowledge base, which includes official MongoDB documentation, curated expert guidance, and other resources provided by MongoDB. These tools enable your AI client to retrieve accurate, version-specific MongoDB information rather than relying on its training data alone.

The following table describes the assistant tools:

MCP Server Assistant Tool Name Description
list-knowledge-sources Lists available data sources in the MongoDB Assistant knowledge base. Use this to explore available data sources or to find search filter parameters to use in search-knowledge.
search-knowledge Searches for information in the MongoDB Assistant knowledge base. The knowledge base includes official documentation, curated expert guidance, and other resources provided by MongoDB. Supports filtering by data source and version.

Tool Details

For additional information about specific MCP tools, see the following sections.

Vector Search Support

The MongoDB MCP Server supports [MongoDB Vector Search](https://www.mongodb.com/docs/vector-search.md#std-label-avs-overview. You can create and manage vector search indexes, generate embeddings, and run semantic search queries through natural language prompts. The following table summarizes key features.

Use Case Example Prompts Relevant Tools
Manage indexes Create a vector search index on the sample_db database and products collection``Show me all vector search indexes on the products collection``Drop the vector search index named vector_index create-index``collection-indexes``drop-indexIMPORTANT: To update a vector search index, drop the existing index and create a new one.
Insert documents with automatic embeddings Insert these documents into the products collection and embed their descriptions insert-many
Vector search queries Search for documents semantically similar to this description``Find me related products filtered by price range aggregate

Use the following resources to learn more:

  • For descriptions of the database tools used for vector search, such as create-index, collection-indexes, drop-index, insert-many, and aggregate, see the [MCP Server Database Tools](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-database-tools table.

  • For detailed usage examples and sample outputs, see [Vector Search.](https://www.mongodb.com/docs/mcp-server/examples.md#std-label-mcp-server-vector-search-examples

  • To configure the MCP Server for vector search, see [voyageApiKey.](https://www.mongodb.com/docs/mcp-server/local-mcp/configuration/options.md#std-label-mcp-voyage-api-key

  • To learn more about vector search, see [MongoDB Vector Search Overview.](https://www.mongodb.com/docs/vector-search.md#std-label-avs-overview

Lexical Search Support

The MongoDB MCP Server supports [MongoDB Search](https://www.mongodb.com/docs/search.md#std-label-atlas-search. You can create and manage lexical search indexes and run full-text search queries through natural language prompts. This includes queries that use the [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search aggregation stage to return relevant documents and the [$searchMeta](https://www.mongodb.com/docs/search/query/aggregation-stages/searchMeta.md#mongodb-pipeline-pipe.-searchMeta aggregation stage to return metadata about the results, such as a count of the relevant documents. The following table summarizes key features.

Use Case Example Prompts Relevant Tools
Manage indexes Create a lexical search index on the sample_db database and products collection for the description field``Show me all lexical search indexes on the products collection``Drop the lexical search index named search_index create-index``collection-indexes``drop-indexIMPORTANT: To update a lexical search index, drop the existing index and create a new one.
Lexical search queries Find products most relevant to “wireless headphones”``Which documents mention “bluetooth”? Include documents where “bluetooth” may be misspelled``Find products that use “wireless” in the description, and treat “bluetooth” as a synonym to “wireless” aggregate

Use the following resources to learn more:

  • For descriptions of the database tools used for lexical search, such as create-index, collection-indexes, drop-index, and aggregate, see the [MCP Server Database Tools](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-database-tools table.

  • For detailed usage examples and sample outputs, see [Lexical Search.](https://www.mongodb.com/docs/mcp-server/examples.md#std-label-mcp-server-lexical-search-examples

  • To learn more about how MongoDB performs lexical searches, see [MongoDB Search Overview.](https://www.mongodb.com/docs/search.md#std-label-atlas-search

Atlas Stream Processing Support

You can use the MongoDB MCP Server to interact with your Atlas Stream Processing resources. Atlas Stream Processing enables you to continuously read, write, and transform streams of data using the same aggregation operations used by Atlas databases. You incur charges for all running processors.

The Atlas Stream Processing tools (atlas-streams-discover, atlas-streams-build, atlas-streams-manage, and atlas-streams-teardown) are available on both the Local and Atlas Managed MCP Servers. For tool descriptions, see the [MCP Server Atlas Tools](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-atlas-tools table.

For more information, see [Atlas Stream Processing.](https://www.mongodb.com/docs/atlas/atlas-stream-processing.md#std-label-atlas-sp

Generate Embeddings Automatically

The MongoDB MCP Server supports two independent mechanisms for generating vector embeddings:

  • MCP Server embedding generation: The MCP Server generates embeddings client-side using Voyage AI before sending documents to MongoDB. This requires a [Voyage AI API key.](https://www.mongodb.com/docs/mcp-server/local-mcp/configuration/options.md#std-label-mcp-voyage-api-key

  • MongoDB automated embedding: MongoDB automatically generates embeddings server-side when documents are inserted or updated. To learn more, see [MongoDB Automated Embedding.](https://www.mongodb.com/docs/mcp-server/tools.md#std-label-mcp-autoembed

These mechanisms work independently and can be used together or separately depending on your use case.

MCP Server embedding generation requires a [Voyage AI API key](https://www.mongodb.com/docs/mcp-server/local-mcp/configuration/options.md#std-label-mcp-voyage-api-key configured on the local MCP Server and is therefore available only on the local MCP Server. MongoDB automated embedding generates embeddings within MongoDB and does not depend on MCP Server configuration.

MCP Server Embedding Generation

If you configure the MCP Server with a [Voyage AI API key](https://www.mongodb.com/docs/mcp-server/local-mcp/configuration/options.md#std-label-mcp-voyage-api-key, the server can automatically generate embeddings in the following ways:

  • Generate embeddings for documents: Embeds text fields in documents when using the insert-many tool.

  • Generates embeddings for queries: Embeds the search query when running vector search queries with the aggregate tool. Specifically, the server generates embeddings for the queryVector parameter in [$vectorSearch](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch aggregation queries.

The MCP Server supports the following Voyage AI embedding models:

  • voyage-3-large

  • voyage-3.5

  • voyage-3.5-lite

  • voyage-code-3

To learn more about Voyage AI models, see the Voyage AI documentation.

Note:

By default, the MongoDB MCP Server validates that fields with vector search indexes contain valid vector embeddings to prevent breaking vector search indexes. To disable this behavior, set the disableEmbeddingsValidation option to true. To learn more, see [voyageApiKey.](https://www.mongodb.com/docs/mcp-server/local-mcp/configuration/options.md#std-label-mcp-voyage-api-key

MongoDB Automated Embedding

Important:

Automated embedding in vector search indexes is available as a preview feature only for MongoDB Community Edition v8.2 and later. The feature and the corresponding documentation might change at anytime during the Preview period.

MongoDB can also automatically generate embeddings server-side when documents are inserted or updated. This approach does not require a Voyage AI API key in your MCP configuration because embedding generation happens within MongoDB.

The MongoDB MCP Server supports managing vector search indexes with automated embedding, which automatically generate embeddings for your documents. You can create, drop, and inspect vector search indexes with automated embedding through natural language prompts. The following table summarizes key features.

To learn more, see Automated Embedding.

Use Case Relevant Tools Example Prompts
Manage indexes create-index``collection-indexes``drop-index “Create an auto embed vector search index on 'plot' field in 'mflix.movies' namespace using voyage-4-large model.”
Insert data with embeddings insert-many “Insert these documents with the following fields and automatically generate embeddings...”
Query with embeddings aggregate “Run a vector search query on mflix.movies with the auto embed index on the 'plot' field.”

Considerations

The MongoDB MCP Server supports pre-filtering vector search queries. To learn more, see [Pre-Filter.](https://www.mongodb.com/docs/search/query/operators-collectors/vectorSearch.md#std-label-fts-vectorSearch-pre-filter

The MCP server does not support the quantization field for vector search indexes.

Performance Advisor Tool

The atlas-get-performance-advisor tool allows you to access [Performance Advisor](https://www.mongodb.com/docs/atlas/performance-advisor.md#std-label-performance-advisor recommendations through natural language queries. This tool helps you identify performance optimization opportunities by analyzing slow queries and suggesting improvements.

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.

Note:

This tool requires [Project Read Only](https://www.mongodb.com/docs/atlas/reference/user-roles.md#mongodb-authrole-Project-Read-Only access or higher and an M10+ cluster.

[Project Read Only](https://www.mongodb.com/docs/atlas/reference/user-roles.md#mongodb-authrole-Project-Read-Only refers to the Atlas Project-level role assigned to your service account, not a database role.

Use Case Example Prompts Performance Advisor Operation
Analyze Slow Queries Show me my slow queries``What is slowing down my cluster?``Show me queries that are longer than 5 seconds``Show me slow writes in the website.users namespace slowQueryLogs
Index Suggestions Are there any indexes I should create to improve performance?``What indexes do you recommend I drop? suggestedIndexes dropIndexSuggestions
Schema Advice Show schema recommendations for my cluster``Help me optimize my database schema schemaAdvice

For detailed usage examples and sample outputs, see [Performance Optimization.](https://www.mongodb.com/docs/mcp-server/examples.md#std-label-mcp-server-performance-examples

Connecting without Atlas Connection String

This section describes the local MCP Server atlas-connect-cluster flow. If you configure the local MCP Server without specifying a connection string to an Atlas cluster, the atlas-connect-cluster tool uses the configured Atlas API service account credentials to create a temporary [database user](https://www.mongodb.com/docs/atlas/security-add-mongodb-users.md#std-label-mongodb-users and establish a connection to the cluster.

The Remote MCP Server uses a different tool, remote-atlas-connect, which follows its own connection and session model and returns a connection handle for subsequent database operations.

The temporary database user has the following characteristics:

  • Randomly generated username and password.

  • By default, expires after 4 hours.

  • Role assigned based on how you configured the MCP Server:

  • [readAnyDatabase](https://www.mongodb.com/docs/manual/reference/built-in-roles.md#mongodb-atlasrole-readAnyDatabase if you enabled [read-only mode](https://www.mongodb.com/docs/mcp-server/local-mcp/configuration/enable-or-disable-features.md#std-label-mcp-server-configuration-read-only-mode or disabled the create, delete, and update tool categories.

  • [readWriteAnyDatabase](https://www.mongodb.com/docs/manual/reference/built-in-roles.md#mongodb-atlasrole-readWriteAnyDatabase if the server has full permissions.

Note:

The MongoDB MCP Server stores user credentials in memory only and never returns or exposes the credentials in the LLM context.

Learn More

To disable specific tools and restrict the MCP Server to read-only mode, see [Local MCP Server Configuration.](https://www.mongodb.com/docs/mcp-server/local-mcp/configuration.md#std-label-mcp-server-configuration

To see some MCP Server example natural language prompts, see [MongoDB MCP Server Usage Examples.](https://www.mongodb.com/docs/mcp-server/examples.md#std-label-mcp-server-examples