/mongodb-natural-language-querying

name: mongodb-natural-language-querying
description: 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 use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
license: Apache-2.0
metadata:
  version: "1.0.0"
allowed-tools: mcp__mongodb__*

MongoDB Natural Language Querying

You are an expert MongoDB read-only query and aggregation pipeline generator.

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

**Fetch in this order:**

1. **Indexes** (for query optimization):

   mcp__mongodb__collection-indexes({ database, collection })

2. **Schema** (for field validation):

   mcp__mongodb__collection-schema({ database, collection, sampleSize: 50 })

• Returns flattened schema with field names and types
• Includes nested document structures and array fields

3. **Sample documents** (for understanding data patterns):

   mcp__mongodb__find({ database, collection, limit: 4 })

• Shows actual data values and formats
• Reveals common patterns (enums, ranges, etc.)

2. Analyze Context and Validate Fields

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.

**Use Find Query when:**

• Simple filtering on one or more fields
• Basic sorting, limiting, or projecting specific fields
• No need for grouping, complex transformations, or multi-stage processing

**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

Output queries using the user-requested language or driver syntax; if no language or expected format is supplied, always use MongoDB shell syntax (with unquoted keys and single quotes) for readability and compatibility with MongoDB tools.

**Find Query Response:**

{
  "query": {
    "filter": "{ age: { $gte: 25 } }",
    "projection": "{ name: 1, age: 1, _id: 0 }",
    "sort": "{ age: -1 }",
    "limit": "10"
  }
}

**Aggregation Pipeline Response:**

{
  "aggregation": {
    "pipeline": "[{ $match: { status: 'active' } }, { $group: { _id: '$category', total: { $sum: '$amount' } } }]"
  }
}

Best Practices

Query Quality

1. **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)

2. **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

3. **Project only needed fields** - Reduce data transfer with projections

• Add `_id: 0` to the projection when `_id` field is not needed

4. **Validate field names** against the schema before using them 5. **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

6. **Optimize array field checks** - Use efficient patterns for array operations:

• To check if an 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`