Mongodb Natural Language Querying
Generate read-only MongoDB queries (find) or aggregation pipelines using natural...
Install
npx skills add fcakyon/claude-codex-settings --skill mongodb-natural-language-querying -g -a claude-code -y- Repository
- fcakyon/claude-codex-settings1.2k
- Category
- Data and ML
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 2,164 tokens
- Runs scripts
- No, instructions only
What it does
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.
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-databasesandmcp__mongodb__list-collectionsif 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
