MongoDB Community✦ Lab Verified
MongoDB
Query and manage MongoDB databases. Perform CRUD operations, run aggregations, and inspect collections.
8.9/10
Score
5ms
Latency
Local
Uptime
28
Tools
stdio
Auth
Quick Verdict
Avoid it if you need index management since drop-index and collection-indexes fail on non-existent collections. Use this for database operations and queries. Best area: core MongoDB reads with 5ms median latency. Biggest failure: index operations break without proper collection validation.
Lab Review
What We Found
What works: MongoDB's core database and collection management shines. Operations like list-databases, collection-schema and find returned clean results consistently in current tests. Complex queries through aggregate and data modifications via insert-many and update-many completed without errors. The server supports local_stdio transport and handled 18 out of 21 tools reliably. Where it breaks: Collection metadata tools fail when collections don't exist yet. Both collection-indexes and collection-storage-size threw "cannot be determined because the collection..." errors on our test collection. The drop-index tool failed with "index not found with name [name_1]" - it doesn't validate index existence before attempting deletion. These metadata failures hit workflows that query collection properties before data insertion. What this means for your workflow: Database queries, schema inspection and data manipulation operations performed consistently in testing. You need error handling around collection-indexes and collection-storage-size calls, plus index existence checks before using drop-index. For applications doing standard CRUD operations and aggregations, this server is solid. For tools requiring collection metadata before operations, build validation layers first.
Lab Observations
What actually happened during testing
During testing, our scanner interacted with MongoDB. 18 tools succeeded, 3 failed.
| Tool | Status |
|---|---|
| collection-indexes | ❌ error |
| collection-storage-size | ❌ error |
| drop-index | ❌ error |
| list-databases | ✅ success |
| mongodb-logs | ✅ success |
| list-knowledge-sources | ✅ success |
| search-knowledge | ✅ success |
| list-collections | ✅ success |
| db-stats | ✅ success |
| collection-schema | ✅ success |
| count | ✅ success |
| find | ✅ success |
| aggregate | ✅ success |
| explain | ✅ success |
| create-collection | ✅ success |
| insert-many | ✅ success |
| create-index | ✅ success |
| update-many | ✅ success |
| export | ✅ success |
| delete-many | ✅ success |
| rename-collection | ✅ success |
Lab Findings
Where it breaks
Other Errors
2x2 tool(s) returned errors
Affected: collection-indexes, collection-storage-size
Missing Resources
1x1 tool(s) returned resource not found
Affected: drop-index
Reliability
Live test completed — 21 of 28 tools executed Score based on transport stability and schema completeness.
Score Breakdown
Reliability
18 of 21 executed tools succeeded.
Security
Score based on schema analysis and dependency audit.
Setup
Local stdio server. Install via npx or binary, no auth required.
Docs
28 tools with descriptions and input schemas.
Compatibility
Standard MCP protocol. Transport: stdio.
Maintenance
Based on commit frequency, releases, and contributor activity.
Tools
28 available tools
Run an aggregation against a MongoDB collection
Describe the indexes for a collection
Describe the schema for a collection
Gets the size of the collection
Connect to a MongoDB instance. The config resource captures if the server is already connected to a MongoDB cluster. If the user has configured a connection string or has previously called the connect tool, a connection is already established and there's no need to call this tool unless the user has explicitly requested to switch to a new MongoDB cluster.
Show all 28 tools →Show less ↑
Gets the number of documents in a MongoDB collection using db.collection.count() and query as an optional filter parameter
Creates a new collection in a database. If the database doesn't exist, it will be created automatically.
Create an index for a collection
Returns statistics that reflect the use state of a single database
Removes all documents that match the filter from a MongoDB collection
Removes a collection or view from the database. The method also removes any indexes associated with the dropped collection.
Removes the specified database, deleting the associated data files
Drop an index for the provided database and collection.
Returns statistics describing the execution of the winning plan chosen by the query optimizer for the evaluated method
Export a query or aggregation results in the specified EJSON format.
Run a find query against a MongoDB collection
Insert an array of documents into a MongoDB collection. If the list of documents is above com.mongodb/maxRequestPayloadBytes, consider inserting them in batches.
List all collections for a given database
List all databases for a MongoDB connection
Returns the most recent logged mongod events
Renames a collection in a MongoDB database
Updates all documents that match the specified filter for a collection. If the list of documents is above com.mongodb/maxRequestPayloadBytes, consider updating them in batches.
Connect to a MongoDB Atlas Local deployment
Create a MongoDB Atlas local deployment. Default image is preview. When the user does not specify an image tag, inform them that preview is used by default and provide this link for more information: https://hub.docker.com/r/mongodb/mongodb-atlas-local
Delete a MongoDB Atlas local deployment
List MongoDB Atlas local deployments
List 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 for information in the MongoDB Assistant knowledge base. This includes official documentation, curated expert guidance, and other resources provided by MongoDB. Supports filtering by data source and version.
FAQ
Frequently asked questions about MongoDB
What specific error patterns occurred with index and storage operations?+
Index operations showed mixed results during testing. collection-indexes and drop-index both failed with 2ms response times, while create-index completed successfully in 39ms. collection-storage-size also failed at 2ms. These failures were limited to specific operations rather than affecting the broader indexing functionality.
How does latency vary between knowledge operations and direct database queries?+
search-knowledge required 1458ms to complete semantic search operations, while list-knowledge-sources finished in 491ms. Direct MongoDB operations like find, aggregate, and count consistently executed under 10ms. Knowledge operations involve computational complexity beyond simple database lookups, explaining the latency differences we measured.
Which MongoDB administrative operations were available through the server?+
Database administration tools functioned across multiple levels. list-databases and db-stats provided cluster-level information, while list-collections and collection-schema offered collection-level details. mongodb-logs delivered server log access. Operations completed within 6ms except for log retrieval which took 10ms during our testing session.
What data modification capabilities did the server provide in sandbox mode?+
Data manipulation tools operated successfully within the sandbox environment. insert-many, update-many, and delete-many all executed with 3ms or less latency. create-collection established new collections in 35ms. rename-collection completed collection renaming in 2ms. Export functionality also worked through the export tool at 2ms.
How did DDL operations perform compared to DML operations?+
Data Definition Language operations required more time than Data Manipulation Language operations. create-collection took 35ms and create-index needed 39ms to establish persistent database structures. DML operations like insert-many, update-many, and delete-many consistently completed within 3ms during our test execution.
What query analysis and performance tools were accessible?+
explain provided query execution plan analysis completing in 2ms. aggregate pipeline operations executed in 4ms. count document counting finished in 2ms. These query analysis tools delivered rapid feedback for database performance evaluation and query optimization workflows.
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Testing History
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