Mongodb atlas cli

MongoDB Atlas CLI Skill

Comprehensive coverage of MongoDB Atlas operations w/ strict safety guarantees. Optimized for the loop: read Performance Advisor → review suggestions → create the right indexes safely. Never destructive.

When to Use

Invoke when the user wants to:

• Connect to Atlas, list clusters/processes/projects
• Pull Performance Advisor output (slow queries, suggested indexes, schema advice, drop hints)
• Read cluster/process/database metrics, alerts, backup status
• List existing indexes w/ live access counts (mongosh-based)
• Explain a query (find or aggregate)
• Create indexes — single, compound, TTL, sparse, partial, unique, geospatial, hashed, Atlas Search, Vector Search
• Diff two audits week-over-week
• Audit overall cluster performance
• Diagnose slow queries before code changes

Do NOT use for any destructive op. Direct CRUD against data is also out of scope (use mongosh directly or the official MongoDB MCP server — see Integration).

Hard Safety Boundaries

Category	Op	Action
Read	clusters/processes/metrics/logs/projects	Allowed, no confirm
Read	performanceAdvisor (all subcmds)	Allowed, no confirm
Read	api performanceAdvisor list*	Allowed, no confirm
Read	alerts/events/backups list, dbusers list	Allowed, no confirm
Read	clusters search indexes list	Allowed, no confirm
Read	mongosh: getIndexes, $indexStats, .explain(), getProfilingStatus	Allowed, no confirm
Write (additive)	clusters indexes create	Requires --confirm + dry-run + duplicate pre-flight
Write (additive)	clusters search indexes create	Requires --confirm + dry-run
Destructive	delete, drop, terminate, pause, restore, kill, force	REFUSE — never run, even w/ user request
Destructive	dbusers create/update/delete	REFUSE — auth changes can lock prod
Destructive	networking * writes, accessLists delete	REFUSE — can sever connectivity
Destructive	backups restore / snapshot delete	REFUSE — overwrites data
Destructive	clusters indexes delete	REFUSE — even if advisor suggests dropping; only INFORM the user

If user asks to drop an index based on listDropIndexSuggestions, surface the suggestion and tell them to do it manually in mongosh / Atlas UI. Never run it.

Local Mongo vs. Atlas Cloud

This skill works against both, but coverage differs. Performance Advisor, metrics, backups, alerts, Atlas Search are cloud-only. mongosh-based scripts (list_indexes, explain_query, profiler_status) work everywhere. Full matrix in references/local-vs-atlas.md.

For local self-hosted mongo, safe_index_create.py --print-mongosh outputs the equivalent createIndex() snippet you can run directly in mongosh.

Prerequisites

Install

# macOS — both atlas CLI + mongosh
brew install mongodb-atlas

# Linux — see https://www.mongodb.com/docs/atlas/cli/current/install-atlas-cli/

# Docker fallback (any OS)
docker pull mongodb/atlas
alias atlas='docker run --rm -it -v ~/.config/atlas:/root/.config/atlas mongodb/atlas atlas'

# Verify
atlas --version && mongosh --version

validate_env.py --install auto-installs via brew on macOS.

Authenticate (Atlas Admin API)

Pick one (priority order):

# 1. Env vars (recommended for automation)
# Preferred (Atlas CLI 1.51+): Service Accounts (OAuth2)
export MONGODB_ATLAS_CLIENT_ID=<client_id>
export MONGODB_ATLAS_CLIENT_SECRET=<client_secret>
# Legacy: programmatic API keys
export MONGODB_ATLAS_PUBLIC_API_KEY=<public_key>
export MONGODB_ATLAS_PRIVATE_API_KEY=<private_key>
export MONGODB_ATLAS_PROJECT_ID=<24-hex-project-id>

# 2. Profile config
atlas config init

# 3. Browser
atlas auth login

API key role: Project Read Only for reads, Project Data Access Admin for index creation. Get keys: Atlas UI → Project Settings → Access Manager → API Keys.

Authenticate (mongosh-based scripts)

For list_indexes.py, explain_query.py, profiler_status.py, safe_index_create.py pre-flight:

export MONGODB_CONNECTION_STRING="mongodb+srv://<host>.mongodb.net/?authSource=admin"
export MONGODB_USERNAME="<atlas-db-user>"
export MONGODB_PASSWORD="<password>"

DB user is separate from API key — Atlas UI → Database Access → ADD NEW DATABASE USER. See references/mongosh-integration.md.

Validate

python3 .claude/skills/mongodb-atlas-cli/scripts/validate_env.py

Script Index

Atlas Admin API (read-only, no DB credentials needed)

Script	Task
validate_env.py	Check CLI install + auth + project access
performance_audit.py	Full read-only audit (orchestrator)
namespaces.py	Performance Advisor → list hot namespaces (entry point)
suggest_indexes.py	Performance Advisor → suggested new indexes
slow_queries.py	Performance Advisor → slow query log lines
schema_advice.py	Performance Advisor → schema anti-pattern recommendations
drop_index_hints.py	Performance Advisor → redundant/hidden/unused indexes (advisory)
cluster_health.py	Process metrics (CPU/mem/conn/op counters/query targeting) — parallel fetches
alerts.py	Active Atlas alerts
backup_status.py	Snapshot list + freshness check
events.py	Project events / audit log
atlas_search_list.py	List Atlas Search & Vector Search indexes
audit_diff.py	Compare two audit JSON snapshots — week-over-week diff

Atlas Admin API (additive write — gated by --confirm)

Script	Task
safe_index_create.py	Create normal index — single/compound/TTL/sparse/partial/unique/2dsphere/hashed
atlas_search_create.py	Create Atlas Search OR Vector Search index

mongosh-based (works on local + Atlas — needs DB user creds)

Script	Task
list_indexes.py	List indexes per coll/db w/ live access counts & sizes — parallel per coll
explain_query.py	Run .explain() on find or aggregate query
profiler_status.py	DB profiler level + recent slow ops captured
index_build_status.py	Poll in-progress index builds via db.currentOp()

All mongosh scripts validate db/collection identifiers before interpolating into JS to prevent injection. Passwords passed via stdin (not argv) to avoid /proc/<pid>/cmdline exposure on Linux.

Pure local

Script	Task
audit_diff.py	Compare audits week-over-week (no Atlas calls)
safe_index_create.py --print-mongosh	Print mongosh createIndex() snippet for local use

All scripts default to dry-run/read-only. Mutations need explicit --confirm.

Common Workflows

1. Optimize from advisor → index

# Full picture
python3 scripts/performance_audit.py --cluster <C> --hours 168

# Verify what's already there
python3 scripts/list_indexes.py --db <DB> --collection <COLL>

# Inspect a specific suggestion's query first
python3 scripts/explain_query.py --db <DB> --collection <COLL> --filter '{...}'

# Create (dry-run)
python3 scripts/safe_index_create.py --cluster <C> --db <DB> --collection <COLL> \
  --key field:1 --name idx_field

# Execute
python3 scripts/safe_index_create.py --cluster <C> --db <DB> --collection <COLL> \
  --key field:1 --name idx_field --confirm

# Re-audit after build completes
python3 scripts/performance_audit.py --cluster <C>

2. Atlas Search for case-insensitive regex queries

When schema advice flags OPTIMIZE_CASE_INSENSITIVE_REGEX_QUERIES:

# See existing search indexes
python3 scripts/atlas_search_list.py --cluster <C> --db <DB> --collection <COLL>

# Generate starter definition
python3 scripts/atlas_search_create.py --init-template search > search.json
# edit search.json

# Dry-run
python3 scripts/atlas_search_create.py --cluster <C> --db <DB> --collection <COLL> \
  --name <coll>_text --file search.json

# Execute
python3 scripts/atlas_search_create.py --cluster <C> --db <DB> --collection <COLL> \
  --name <coll>_text --file search.json --confirm

3. Vector Search for AI / RAG

python3 scripts/atlas_search_create.py --init-template vector > vec.json
# edit vec.json — set numDimensions matching your embedding model
python3 scripts/atlas_search_create.py --cluster <C> --db <DB> --collection <COLL> \
  --name <coll>_vec --file vec.json --type vectorSearch --confirm

4. Drop redundant indexes (manually — skill won't drop)

# Get advisory list
python3 scripts/drop_index_hints.py --cluster <C>

# Verify w/ live counters
python3 scripts/list_indexes.py --db <DB> --collection <COLL>

# Confirm covering index serves a real query
python3 scripts/explain_query.py --db <DB> --collection <COLL> \
  --filter '{<typical_filter>}'

# Hide first (24-48h soak), then drop manually in mongosh
mongosh ... --eval 'db.runCommand({collMod: "<COLL>", index: {name: "<NAME>", hidden: true}})'

5. Weekly automation

# Save snapshot
python3 scripts/performance_audit.py --cluster <C> --hours 168 --json > audits/$(date +%F).json

# Diff vs. last week
python3 scripts/audit_diff.py audits/<old>.json audits/<new>.json --markdown > diff.md

6. Health checks (read-only, run anytime)

python3 scripts/cluster_health.py --cluster <C>
python3 scripts/backup_status.py --cluster <C>
python3 scripts/alerts.py --status OPEN

Index Types Supported

safe_index_create.py covers every standard MongoDB index type:

Type	Flag combination	Atlas-only?
Single-field	--key field:1	no
Compound	--key f1:1 --key f2:-1 ...	no
TTL	--key created_at:1 --ttl-seconds N	no
Sparse	--sparse	no
Partial	--partial-filter pf.json	no
Unique	--unique	no
Combined (e.g. unique + partial)	--unique --partial-filter pf.json	no
Geospatial 2dsphere	--key location:2dsphere	no
Hashed (sharding)	--key user_id:hashed	no
Text (legacy)	--key field:text	no
Wildcard	--file <json> w/ {"keys": [{"$**": 1}]}	no
Atlas Search	atlas_search_create.py --type search	yes
Vector Search	atlas_search_create.py --type vectorSearch	yes

Full reference: references/index-types.md.

Pre-flight Duplicate Detection

safe_index_create.py runs a pre-flight via mongosh (when creds are available) that checks if:

• An index w/ identical keys already exists → refuses to create duplicate
• An existing larger compound already covers the proposed keys as a prefix → refuses (your index would be redundant)

Override w/ --skip-preflight (not recommended). If mongosh creds aren't available, pre-flight is silently skipped.

Output Format

Default: human-readable summary to stderr, JSON to stdout when --json flag set. Pipe through jq:

python3 scripts/suggest_indexes.py --cluster <C> --json | jq '.[] | .namespace'

Error Handling

When scripts detect missing credentials or permission errors, they output:

MISSING: <ENV_VAR>
ASK_USER: <human prompt>
LOCATION: <where to find the value>

Claude should parse & use AskUserQuestion to prompt.

For permission errors:

PERMISSION_DENIED: requires <role>
ACTION: Ask user to grant role or use a different API key

Refusal Pattern

Refused destructive ops produce:

REFUSED: `atlas <command>` is a destructive op blocked by this skill.
  Matched forbidden prefix: <prefix>
  Run it manually in Atlas UI or via `atlas` directly if you intend to.

Never bypass — even w/ "I'm sure", "force it", "yolo". Tell the user to run it manually.

Complementary Tools

This skill focuses on admin/audit/index-management. For complementary capabilities:

Need	Tool
AI-assisted CRUD against data, NL → query, schema introspection	MongoDB MCP Server
Atlas Stream Processing pipelines	jwongmongodb/atlas-streams-skills
Connection pool / driver tuning	MongoDB Agent Skills — Connection
Schema design from scratch	sibling pydantic-model skill in this repo

References

Topic	File
Full Atlas CLI command map	references/cli-commands.md
Performance Advisor deep-dive	references/performance-advisor.md
Index strategy (ESR, etc.)	references/index-strategy.md
Every index type w/ examples	references/index-types.md
Atlas Search + Vector Search	references/atlas-search.md
Local vs. Atlas feature matrix	references/local-vs-atlas.md
mongosh integration details	references/mongosh-integration.md
Quick recipes (copy-paste)	references/quick-recipes.md
Safety boundaries detail	references/safety-boundaries.md
Troubleshooting	references/troubleshooting.md

Self-Healing

On any atlas error: atlas <command> --help → if unclear, WebFetch https://www.mongodb.com/docs/atlas/cli/current/command/atlas-<command>/ (dashes join subcommands) → adjust → re-run.

Integration

Pairs w/:

• senior-backend — schema/query design before adding indexes
• code-quality — review query code that produces slow ops
• pydantic-model — model-level fixes that reduce doc bloat
• MongoDB MCP server — for direct data ops