list_databases

List all databases in the MongoDB server.

list_collections

List all collections in a database. Start here to understand what collections are available before querying.

get_schema

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 }

query

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": "," } }

aggregate

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 - $lookup: Perform left outer joins - $unwind: Deconstruct array fields Unsafe/Blocked Stages: - $out: Write results to collection - $merge: Merge results into collection - $addFields: Add new fields - $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 }} ] }

get_collection_stats

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_indexes

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

explain_query

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.

get_distinct_values

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 } }

sample_data

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": "," } }

count_documents

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 } } }

find_by_ids

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 } }

geo_query

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]] ] } }

text_search

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 }