MongoDB Agent Skills is an official collection of skills and plugins that help AI coding agents work with MongoDB databases, including Atlas and self-managed deployments. Developers use it for query writing, schema design, query optimization, Atlas Search, and vector search. The catalogue entries are MongoDB’s own agent skills, plugins, and setup instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mongodb/agent-skills --skill mongodb-natural-language-queryinggit clone --depth 1 https://github.com/mongodb/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mongodb/agent-skills/mongodb-natural-language-querying)<a href="https://agentmods.dev/skills/mongodb/agent-skills/mongodb-natural-language-querying"><img src="https://agentmods.dev/badge/skills/mongodb/agent-skills/mongodb-natural-language-querying/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mongodb/agent-skills/mongodb-natural-language-querying"><img src="https://agentmods.dev/badge/skills/mongodb/agent-skills/mongodb-natural-language-querying.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00162 | $0.02210 |
| Opus 5 | $0.00081 | $0.01105 |
| Sonnet 5 | $0.00032 | $0.00442 |
| Haiku 4.5 | $0.00016 | $0.00221 |
Grade A, and why
mongodb-natural-language-querying scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to mongodb-natural-language-querying — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 196 lines · 162 tokens per session scan A 2761f2df7bc1
mongodb-natural-language-querying is a skill published in the GitHub repository mongodb/agent-skills (182 stars, last pushed yesterday), licensed Apache-2.0. It adds 162 tokens to every session and 2,210 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to mongodb-natural-language-querying, differing in 0 lines, and is treated as a copy.
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