AI agents for MongoDB
A Monmo agent works with MongoDB the way a database-minded engineer would. It answers questions with aggregation pipelines, works through index suggestions, maps how each collection is really shaped and keeps an eye on Atlas alerts and access. It uses the database user and Atlas key you connect, and asks before it creates an index, updates documents or changes users and network access.
Updated 2026-11-13 · Free to start. Paid plans from $15 a month.
What agents do in MongoDB
Answer questions with an aggregation
Ask which plans had the most orders last month and the agent writes the aggregation pipeline, runs it as a read-only user, checks the result against a simple count and gives you the pipeline too.
AskWhich plans had the most orders in October? Group by plan and country, and show me the pipeline.
Work through index suggestions
It reads the Performance Advisor's suggested indexes and the slow operations behind them, checks each against existing indexes and write volume, and recommends which to build. Creating an index waits for you.
AskGo through the Performance Advisor suggestions for the orders cluster and tell me which indexes are worth building.
Map a collection's real shape
It samples documents from a collection and writes down every field, its types and how often it is missing or null, then turns that into a short schema page in Notion for the team.
AskSample the customers collection and document the real fields, their types and how often each one is missing.
Export a filtered set
Ask for every subscription that lapsed this quarter with the account owner's email, and it runs the query and writes the result to Google Sheets with the filter used at the top.
AskExport subscriptions that lapsed this quarter, with owner email and plan, to a Google Sheet.
Fix bad documents carefully
When documents hold a wrong value, such as a country stored as a full name, it counts the matches, shows a sample before and after and drafts the update. It runs only after you approve the card.
AskSome users have country stored as a full name instead of a code. Count them and prepare the fix for my approval.
Report on cluster health
Every Monday it reads the week's Atlas alerts and metrics for connections, operations, replication lag and disk use, and writes a short health note for Slack beside your Datadog view, with anything trending the wrong way.
AskEvery Monday at 9, summarise last week's Atlas alerts and cluster metrics, and flag anything getting worse.
Review users and network access
It lists database users with their roles and the IP access list, flags entries that allow any address and users nobody has used for months, and drafts the tightening for your approval.
AskCheck our Atlas project for database users with too much access and IP entries open to everywhere.
How it connects
- 1You connect a MongoDB database user for queries and, for Atlas metrics and alerts, an Atlas API key or service account for the project. A read role and Project Read Only cover most of the work.
- 2Give MongoDB to your engineering agent and to a data agent that answers questions from it. Agents that never touch your data should not have the connection.
- 3Queries, aggregations, metrics and alerts are read without asking. Inserting, updating or deleting documents, building or dropping an index, and changing users or the access list each ask first.
More in Developer and IT
Other apps of this kind that agents work in, each with its own page.
Questions people ask
Can an AI agent query my MongoDB database?
Yes. Connect a database user with a read role and a Monmo agent can run finds and aggregation pipelines to answer questions in plain words, showing you the query each time. With read-only access it cannot change a document even by mistake. Write access is optional, and every write still waits for your approval.
Should an AI agent create indexes in MongoDB Atlas?
It should recommend them and build them only when you agree. Building an index adds load and takes space, so the AI agent sets out the query it helps, the expected size and the time of day it suggests. It builds the index through the MongoDB connection, or in its own browser when the connection does not cover something.
Can it clean up inconsistent data in a collection?
It can find it and prepare the clean-up. The agent counts the documents affected, shows you before and after on a sample, and drafts the update with its filter. Nothing changes until you allow the card, and large clean-ups can be split into batches you approve in turn.
Does it work with self-hosted MongoDB as well as Atlas?
For queries, yes, if the agent can reach the database with a connection string and a user you create. Atlas adds the parts that come through its API, such as alerts, Performance Advisor and the access list. On a self-hosted server the agent reads what its user is allowed to read.