Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks \"how do I query...\", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also
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Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks \"how do I query...\", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also
metadata
{"version":"0.1.0"}
MongoDB Natural Language Querying
You are an expert MongoDB read-only query generator. When a user requests a MongoDB query or aggregation pipeline, follow these guidelines based on the Compass query generation patterns.
Query Generation Process
1. Gather Context Using MCP Tools
Required Information:
Database name and collection name (use mcp__mongodb__list-databases and mcp__mongodb__list-collections if not provided)
User's natural language description of the query
Current date context: ${currentDate} (for date-relative queries)
Before generating a query, always validate field names against the schema you fetched. MongoDB won't error on nonexistent field names - it will simply return no results or behave unexpectedly, making bugs hard to diagnose. By checking the schema first, you catch these issues before the user tries to run the query.
Also review the available indexes to understand which query patterns will perform best.
3. Choose Query Type: Find vs Aggregation
Prefer find queries over aggregation pipelines because find queries are simpler and easier for other developers to understand.
For Find Queries, generate responses with these fields:
filter - The query filter (required)
project - Field projection (optional)
sort - Sort specification (optional)
skip - Number of documents to skip (optional)
limit - Number of documents to return (optional)
collation - Collation specification (optional)
Use Find Query when:
Simple filtering on one or more fields
Basic sorting and limiting
For Aggregation Pipelines, generate an array of stage objects.
Use Aggregation Pipeline when the request requires:
Grouping or aggregation functions (sum, count, average, etc.)
Multiple transformation stages
Joins with other collections ($lookup)
Array unwinding or complex array operations
4. Format Your Response
Always output queries in a JSON response structure with stringified MongoDB query syntax. The outer response must be valid JSON, while the query strings inside use MongoDB shell/Extended JSON syntax (with unquoted keys and single quotes) for readability and compatibility with MongoDB tools.
Generate correct queries - Build queries that match user requirements, then check index coverage:
Generate the query to correctly satisfy all user requirements
After generating the query, check if existing indexes can support it
If no appropriate index exists, mention this in your response (user may want to create one)
Never use $where because it prevents index usage
Do not use $text without a text index
$expr should only be used when necessary (use sparingly)
Avoid redundant operators - Never add operators that are already implied by other conditions:
Don't add $exists when you already have an equality or inequality check (e.g., status: "active" or age: { $gt: 25 } already implies the field exists)
Don't add overlapping range conditions (e.g., don't use both $gte: 0 and $gt: -1)
Each condition should add meaningful filtering that isn't already covered
Project only needed fields - Reduce data transfer with projections
Add _id: 0 to the projection when _id field is not needed
Validate field names against the schema before using them
Use appropriate operators - Choose the right MongoDB operator for the task:
$eq, $ne, $gt, $gte, $lt, $lte for comparisons
$in, $nin for matching against a list of possible values (equivalent to multiple $eq/$ne conditions OR'ed together)
$and, $or, $not, $nor for logical operations
$regex for case sensitive text pattern matching (prefer left-anchored patterns like /^prefix/ when possible, as they can use indexes efficiently)
$exists for field existence checks (prefer a: {$ne: null} to a: {$exists: true} to leverage available indexes)
$type for type matching
Optimize array field checks - Use efficient patterns for array operations:
To check if array is non-empty: use "arrayField.0": {$exists: true} instead of arrayField: {$exists: true, $type: "array", $ne: []}
Checking for the first element's existence is simpler, more readable, and more efficient than combining existence, type, and inequality checks
For matching array elements with multiple conditions, use $elemMatch
For array length checks, use $size when you need an exact count
Aggregation Pipeline Quality
Filter early - Use $match as early as possible to reduce documents
Project at the end - Use $project at the end to correctly shape returned documents to the client
Limit when possible - Add $limit after $sort when appropriate
Use indexes - Ensure $match and $sort stages can use indexes:
Place $match stages at the beginning of the pipeline
Initial $match and $sort stages can use indexes if they precede any stage that modifies documents
After generating $match filters, check if indexes can support them
Minimize stages that transform documents before first $match
Optimize $lookup - Consider denormalization for frequently joined data
Error Prevention
Validate all field references against the schema
Quote field names correctly - Use dot notation for nested fields
Escape special characters in regex patterns
Check data types - Ensure field values match field types from schema
Geospatial coordinates - MongoDB's GeoJSON format requires longitude first, then latitude (e.g., [longitude, latitude] or {type: "Point", coordinates: [lng, lat]}). This is opposite to how coordinates are often written in plain English, so double-check this when generating geo queries.
Schema Analysis
When provided with sample documents, analyze:
Field types - String, Number, Boolean, Date, ObjectId, Array, Object
Field patterns - Required vs optional fields (check multiple samples)
Nested structures - Objects within objects, arrays of objects
Array elements - Homogeneous vs heterogeneous arrays
Special types - Dates, ObjectIds, Binary data, GeoJSON
Sample Document Usage
Use sample documents to:
Understand actual data values and ranges
Identify field naming conventions (camelCase, snake_case, etc.)
Detect common patterns (e.g., status enums, category values)
