Mongodb Natural Language Querying

Turns natural language into MongoDB find queries or aggregation pipelines by first pulling collection schemas, indexes, and sample documents through the MongoDB MCP server. It validates field names against the schema before generating anything, which saves you from the silent failures MongoDB loves to give when you typo a field. The skill defaults to simple find queries over aggregations when possible and includes practical guardrails like avoiding redundant operators and filtering early in pipelines. Worth noting it only handles read operations and won't touch Atlas Search, vector queries, or query optimization, so scope is deliberately narrow here.

skills.sh installs: 4.9K
GitHub stars: 197
last updated: Aug 21, 2026

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Install

$ npx -y skills add mongodb/agent-skills --skill mongodb-natural-language-querying --agent claude-code

Installs into .claude/skills of the current project.

Files

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.)