Mongodb Natural Language Querying

MongoDBOfficialPublished by MongoDB

Skill
Tool integration
Data and AI
Apache-2.0
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From the author

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.

— the description by MongoDB

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as an expert assistant for interacting with MongoDB databases. It leverages MCP tools to inspect collection schemas, indexes, and sample documents to ensure generated queries are accurate, performant, and syntactically correct. It focuses on read-only operations, providing either standard find queries or complex aggregation pipelines based on user requirements.

What it does

Translates natural language requests into MongoDB find queries or aggregation pipelines.
Automatically fetches and validates collection schemas and indexes before query generation.
Analyzes sample documents to understand data patterns and field types.
Provides optimized query structures while avoiding anti-patterns like $where.
Supports complex operations including grouping, joins, and array manipulations.
Offers guidance on index usage and query performance.

When to use it

When you need to write or generate MongoDB queries from natural language descriptions.
When you are unsure of the correct MongoDB query syntax for a specific filtering task.
When you need to perform data aggregation or grouping in a MongoDB collection.
When you want to translate SQL-like logic into MongoDB query syntax.
When you need to verify if your query requirements align with existing collection schemas.

Advantages

Reduces syntax errors by validating field names against the actual database schema.
Promotes performance by suggesting index-friendly query patterns.
Simplifies complex aggregation logic through automated pipeline construction.
Ensures context-aware generation by analyzing real data samples.
Prevents common pitfalls by adhering to MongoDB best practices.

Usage examples

“Find all active users over 25 years old, sorted by registration date.”: The agent inspects the schema for 'status', 'age', and 'registrationDate', then returns a JSON-formatted find query with the appropriate filter and sort parameters.
“How do I calculate the total amount per category for active orders?”: The agent generates an aggregation pipeline using $match to filter for 'active' status and $group to sum the 'amount' field by 'category'.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/mongodb/mongodb-natural-language-querying/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip mongodb-natural-language-querying.zip -d .agents/skills

Documentation by 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:

Indexes (for query optimization):

mcp__mongodb__collection-indexes({ database, collection })

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
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

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)
- $type for type matching
Optimize array field checks - Use efficient patterns for array operations:
- To check if an array is non-empty: use "arrayField.0": {$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 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)
Estimate cardinality for grouping operations
Validate that your query will work with real data

Error Handling

If you cannot generate a query:

Explain why - Missing schema, ambiguous request, impossible query
Ask for clarification - Request more details about requirements
Suggest alternatives - Propose different approaches if available
Provide examples - Show similar queries that could work

Example Workflow

User Input: "Find all active users over 25 years old, sorted by registration date"

Your Process:

Check schema for fields: status, age, registrationDate or similar
Verify field types match the query requirements
Generate query based on user requirements
Check if available indexes can support the query
Suggest creating an index if no appropriate index exists for the query filters

Generated Query:

{
  "query": {
    "filter": "{ status: 'active', age: { $gt: 25 } }",
    "sort": "{ registrationDate: -1 }"
  }
}

Managing Context Size

Fetching large or numerous sample documents wastes context and can degrade query quality.

Adjust sample count by schema width:

< 30 fields: limit: 4 (default)
30–80 fields: limit: 2
80–150 fields: limit: 1
150+ fields: limit: 1 with a projection of only the fields relevant to the user's query

Preview large array fields and strings:

If schema documents contains arrays, use $slice: 3 in the sample projection to cap array size. Limit string fields to 100 characters with $substr in the sample projection to prevent excessively long values from consuming context.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs an MCP server

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Attribution and licence

Published by

MongoDB

Publisher

MongoDB (Official)

Source

mongodb/agent-skills · skills/mongodb-natural-language-querying · commit 155bb9c

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

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