Execute a read-only aggregation pipeline on a collection.

Supported Stages:

• $match: Filter documents

• $group: Group documents by a key

• $sort: Sort documents

• $project: Shape the output

• $addFields - Include additional or calculated fields

• $lookup: Perform left outer joins

• $unwind: Deconstruct array fields

Unsafe/Blocked Stages:

• $out: Write results to collection

• $merge: Merge results into collection

• $set: Set field values

• $unset: Remove fields

• $replaceRoot: Replace document structure

• $replaceWith: Replace document

Example - User Statistics by Role: use_mcp_tool with server_name: "mongodb", tool_name: "aggregate", arguments: { "collection": "users", "pipeline": [ { "$match": { "active": true } }, { "$group": { "_id": "$role", "count": { "$sum": 1 }, "avgAge": { "$avg": "$age" } }}, { "$sort": { "count": -1 } } ], "limit": 100 }

Example - Posts with Author Details: use_mcp_tool with server_name: "mongodb", tool_name: "aggregate", arguments: { "collection": "posts", "pipeline": [ { "$match": { "published": true } }, { "$lookup": { "from": "users", "localField": "authorId", "foreignField": "_id", "as": "author" }}, { "$unwind": "$author" }, { "$project": { "title": 1, "authorName": "$author.name", "publishDate": 1 }} ] }

Count documents in a collection that match a filter.

Benefits:

• More efficient than retrieving full documents

• Good for understanding data volume

• Can help planning query strategies

• Optimize pagination implementation

Example: use_mcp_tool with server_name: "mongodb", tool_name: "count_documents", arguments: { "collection": "users", "filter": { "active": true, "age": { "$gte": 21 } } }

Get the execution plan for a query.

Helps understand:

• How MongoDB will execute the query

• Which indexes will be used

• Number of documents examined

• Execution stages and timing

Use this to optimize slow queries.

Find multiple documents by their IDs in a single request.

Advantages:

• More efficient than multiple single document lookups

• Preserves ID order in results when possible

• Can filter specific fields with projection

• Handles both string and ObjectId identifiers

Example: use_mcp_tool with server_name: "mongodb", tool_name: "find_by_ids", arguments: { "collection": "products", "ids": ["5f8d0f3c", "5f8d0f3d", "5f8d0f3e"], "idField": "_id", "projection": { "name": 1, "price": 1 } }

Execute geospatial queries on a MongoDB collection.

Supports:

• Finding points near a location

• Finding documents within a polygon, circle, or box

• Calculating distances between points

• GeoJSON and legacy coordinate pair formats

Requirements:

• Collection must have a geospatial index (2dsphere recommended)

• Coordinates should follow MongoDB conventions (longitude first, then latitude)

Examples:

1. Find locations near a point (2 miles radius): use_mcp_tool with server_name: "mongodb", tool_name: "geo_query", arguments: { "collection": "restaurants", "operation": "near", "point": [-73.9667, 40.78], "maxDistance": 3218.69, // 2 miles in meters "distanceField": "distance" }

2. Find locations within a polygon: use_mcp_tool with server_name: "mongodb", tool_name: "geo_query", arguments: { "collection": "properties", "operation": "geoWithin", "geometry": { "type": "Polygon", "coordinates": [ [[-73.958, 40.8], [-73.94, 40.79], [-73.95, 40.76], [-73.97, 40.76], [-73.958, 40.8]] ] } }

Get detailed statistics about a collection.

Returns information about:

• Document count and size

• Storage metrics

• Index sizes and usage

• Average document size

• Padding factor

Get distinct values for a field in a collection.

Useful for:

• Understanding data distribution

• Finding unique categories

• Data quality checks

• Identifying outliers

Example: use_mcp_tool with server_name: "mongodb", tool_name: "get_distinct_values", arguments: { "collection": "users", "field": "role", "filter": { "active": true } }

Get information about indexes on a collection.

Returns details about:

• Index names and fields

• Index types (single field, compound, text, etc.)

• Index sizes

• Index options

• Usage statistics

Infer schema from a collection by analyzing sample documents.

Best Practice: Use this before querying to understand collection structure.

Example: use_mcp_tool with server_name: "mongodb", tool_name: "get_schema", arguments: { "collection": "users", "sampleSize": 100 }

List all collections in a database.

Start here to understand what collections are available before querying.

List all databases in the MongoDB server.

Execute a read-only query on a collection using MongoDB query syntax.

Supports both JSON and CSV output formats:

• Use outputFormat="json" for standard JSON (default)

• Use outputFormat="csv" for comma-separated values export

Best Practices:

• Use projections to fetch only needed fields

• Add limits for large collections

• Use sort for consistent ordering

Example - Standard Query: use_mcp_tool with server_name: "mongodb", tool_name: "query", arguments: { "collection": "users", "filter": { "age": { "$gte": 21 } }, "projection": { "name": 1, "email": 1 }, "sort": { "name": 1 }, "limit": 100 }

Example - CSV Export: use_mcp_tool with server_name: "mongodb", tool_name: "query", arguments: { "collection": "users", "filter": { "active": true }, "outputFormat": "csv", "formatOptions": { "includeHeaders": true, "delimiter": "," } }

Get a random sample of documents from a collection.

Supports both JSON and CSV output formats:

• Use outputFormat="json" for standard JSON (default)

• Use outputFormat="csv" for comma-separated values export

Useful for:

• Exploratory data analysis

• Testing with representative data

• Understanding data distribution

• Performance testing with realistic data subsets

Example - JSON Sample: use_mcp_tool with server_name: "mongodb", tool_name: "sample_data", arguments: { "collection": "users", "size": 50 }

Example - CSV Export: use_mcp_tool with server_name: "mongodb", tool_name: "sample_data", arguments: { "collection": "users", "size": 100, "outputFormat": "csv", "formatOptions": { "includeHeaders": true, "delimiter": "," } }

Perform a full-text search on a collection.

Requirements:

• Collection must have a text index

• Only one text index per collection is allowed

Features:

• Supports phrases and keywords

• Word stemming

• Stop words removal

• Text score ranking

Example: use_mcp_tool with server_name: "mongodb", tool_name: "text_search", arguments: { "collection": "articles", "searchText": "mongodb database", "filter": { "published": true }, "limit": 10, "includeScore": true }