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Official agent skill

Mongodb Query Optimizer

by mongodb in mongodb/agent-skills

Help with MongoDB query optimization and indexing. An agent skill from mongodb/agent-skills.

OfficialApache-2.0Auto-check passedDatabases

Install Mongodb Query Optimizer

skills CLI
$ npx skills add mongodb/agent-skills --skill mongodb-query-optimizer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mongodb/agent-skills mongodb-query-optimizer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mongodb/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mongodb-query-optimizer .claude/skills/mongodb-query-optimizer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
mongodb-query-optimizer
GitHub stars
190
Used in
2 other repos
Token cost
~2.6k tokens
SKILL.md length
1,228 words
Files
5 (incl. references)
Skills in repo
9
Repo updated
First seen
Licence
Apache-2.0

At a glance

Help with MongoDB query optimization and indexing. An agent skill from mongodb/agent-skills.

  • Works in 2 steps: DB connection string works for MongoDB MCP → Atlas API access works for MongoDB MCP
  • Asks for optimization
  • SKILL.md covers When this skill is invoked, High Level Workflow, MCP: available tools and Example workflow 1 (help with…, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Mongodb Query Optimizer is an agent skill from mongodb/agent-skills, published by the product's own GitHub organization. Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/aggregation-optimization.md`, `references/antipattern-examples.md` and `references/core-indexing-principles.md`). Compatibility notes: Best with MongoDB MCP server. Uses collection-indexes and explain when the connection string works; uses Atlas Performance Advisor when Atlas API is…

It sits in Databases, covering NoSQL databases, Query optimization and MCP servers. It works with MongoDB and Model Context Protocol. The repository describes itself as: Use the official MongoDB Skills with your favorite coding agent to build faster. The licence is Apache-2.0.

When your agent uses it

  • Asks for optimization
  • Performance: How do I optimize this query?
  • How do I index this?
  • Why is this query slow?

Example prompts

  • “How do I optimize this query?”
  • “How do I index this?”
  • “Why is this query slow?”
  • “/mongodb-query-optimizer”

Requirements

  • Compatibility (from SKILL.md): Best with MongoDB MCP server. Uses collection-indexes and explain when the connection string works; uses Atlas Performance Advisor when Atlas API is configured. Without either, suggest indexes from query shape only. User creates indexes in Atlas or migrations unless tooling allows otherwise.

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. DB connection string works for MongoDB MCP
  2. Atlas API access works for MongoDB MCP

What it can do on your machine

Read from SKILL.md and the folder at commit 18b014e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • mongodb.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Best with MongoDB MCP server. Uses collection-indexes and explain when the connection string works; uses Atlas Performance Advisor when Atlas API is configured. Without either, suggest indexes from query shape only. User creates indexes in Atlas or migrations unless tooling allows otherwise.

    From compatibility in the SKILL.md frontmatter.

Context cost

Mongodb Query Optimizer loads about 2.6k tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 118 tokens; SKILL.md has 1,228 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~118
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mongodb/agent-skills at commit 18b014e, republished under its Apache-2.0 licence (© mongodb). 1,228 words, ~2,574 tokens.

Download SKILL.mdSave it as .claude/skills/mongodb-query-optimizer/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
mongodb-query-optimizer
description
Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
compatibility
Best with MongoDB MCP server. Uses collection-indexes and explain when the connection string works; uses Atlas Performance Advisor when Atlas API is configured. Without either, suggest indexes from query shape only. User creates indexes in Atlas or migrations unless tooling allows otherwise.
license
Apache-2.0
metadata.version
1.0.0

MongoDB Query Optimizer

When this skill is invoked

Invoke only when the user wants:

  • Query/index optimization or performance help
  • Why a query is slow or how to speed it up
  • Slow queries on their cluster and/or how to optimize them

Do not invoke for routine query authoring unless the user has requested help with optimization, slow queries, or indexing.

High Level Workflow

General Performance Help

If the user wants to examine slow queries, or is looking for general performance suggestions (not regarding any particular query):

  • Use MongoDB MCP server atlas-get-performance-advisor tool to fetch slow query logs and performance advisor output
  • Make suggestions based on this information

If Atlas MCP Server for Atlas is not configured or you don’t have enough information to run atlas-get-performance-advisor against the correct cluster, tell the user that general performance analysis requires Atlas MCP Server configuration with API credentials, and suggest they configure it or ask about a specific query instead.

Help with a Specific Query

If the user is asking about a particular query:

  • Use collection-indexes, explain, and find MCP tools to get existing indexes on the collection, explain() output for the query, and a sample document from the collection
  • Use atlas-get-performance-advisor MCP tool to fetch slow query logs and performance advisor output

Then make an optimization suggestion based on collected information and MongoDB best practices and examples from reference files. Prefer creating an index that fully covers the query if possible. If you cannot use MongoDB MCP Server then still try to make a suggestion.

MCP: available tools

How to invoke. Call the MongoDB MCP server with the exact tool name as toolName and a single arguments object as arguments. Do not pass the tool name as an option, query param, or nested key; pass it as the MCP tool name and the parameters as the arguments object. Full MCP Server tool reference: MongoDB MCP Server Tools.

Database tools (when the MCP cluster connection works):

Tool name (exact)Arguments object
collection-indexes{ "database": "<db>", "collection": "<coll>" } — both required strings.
explain{ "database": "<db>", "collection": "<coll>", "method": [ { "name": "find", "arguments": { "filter": {...}, "sort": {...}, "limit": N } } ], "verbosity": "executionStats" }. method is an array of one object: name is "find", "aggregate", or "count"; arguments holds that method's params (e.g. find: filter, sort, limit; aggregate: pipeline; count: query). Optional verbosity: "queryPlanner" (default), "executionStats", "queryPlannerExtended", "allPlansExecution".
find{ "database": "<db>", "collection": "<coll>", "filter": {...}, "projection": {...}, "sort": {...}, "limit": N } — database, collection, and filter are required. Optional: projection, sort, limit.

Atlas tools (when Atlas API credentials are configured):

Tool name (exact)Arguments object
atlas-list-projects{} or { "orgId": "<24-char hex>" }. Returns projects with their IDs; use to get projectId for Performance Advisor.
atlas-get-performance-advisorRequired: "projectId" (24-character hex string), "clusterName" (string, 1–64 chars, alphanumeric/underscore/dash). Optional: "operations" — array of strings from "suggestedIndexes", "dropIndexSuggestions", "slowQueryLogs", "schemaSuggestions" (request only what you need); for slowQueryLogs only: "since" (ISO 8601 date-time), "namespaces" (array of "db.coll" strings).

For a user question, try to fetch information from both the connection string and Atlas API related to the query you are optimizing.

1. DB connection string works for MongoDB MCP

Typical flow: call collection-indexes → explain → find (sample doc).

  • collection-indexes — Use the result's classicIndexes (each has name, key) to see if the query can already use an existing index.
  • explain — Run in "queryPlanner" mode first to check for COLLSCAN. If the query uses an index or the collection is very small, run again with "executionStats" (10-second timeout) to get docs scanned vs. returned.
2. Atlas API access works for MongoDB MCP

If you need a project ID, call atlas-list-projects first. Then call atlas-get-performance-advisor with only the operations you need:

Operation valueUse when
slowQueryLogsFetching slow queries—prioritize by slowest and most frequent. Optional: namespaces to scope to a collection; since for a time window.
suggestedIndexesFetching cluster index recommendations
dropIndexSuggestionsUser asks what to remove or reduce index overhead
schemaSuggestionsUser asks for schema/query-structure advice alongside indexes

Do not pass the MCP tool name as an operations value—operations is a separate argument listing what data to fetch.

Show full SKILL.md (570 more words)Show less

Example workflow 1 (help with specific query)

User: "Why is this query slow? db.orders.find({status: 'shipped', region: 'US'}).sort({date: -1})"

If MCP db connection is configured and the database + collection names are known, run steps 1–3. Otherwise skip to step 4.

  1. Check existing collection indexes:

    • Call collection-indexes with database=store, collection=orders
    • Result shows: {_id: 1}, {status: 1}, {date: -1}

  2. Run explain:

    • Call explain with method=find, filter={status: 'shipped', region: 'US'}, sort={date: -1}, verbosity=queryPlanner and executionStats
    • Result: Uses {status: 1} index, then in-memory SORT, totalKeysExamined: 50000, nReturned: 100

  3. Run find:

    • Call find with limit=1 to fetch a sample document to impute the schema.

If MCP Atlas connection is configured, run step 4. Otherwise skip to step 5.

  1. Run atlas-get-performance-advisor:

    • Try to get the cluster name from the MCP connection string, or ask the user for projectId/clusterName
    • Use slowQueryLogs to fetch slow query logs from database=store, collection=orders in the past 24 hours
    • Use suggestedIndexes to check for index suggestions for the query

  2. Diagnose: Based on explain output and slow query logs, this query targets 100 docs but scans 50K index entries (poor selectivity: 0.002). In-memory sort adds overhead. Index doesn't support both filter fields or sort.

  3. Recommend: Create compound index {status: 1, region: 1, date: -1} following ESR (two equality fields, then sort). This eliminates in-memory sort and improves selectivity by filtering on both status and region.

If the MongoDB MCP server is not set up, follow best indexing practices.

Example workflow 2 (general database performance help)

User: "Can you help with optimizing slow queries on my cluster?”

  1. Run atlas-get-performance-advisor:
    • Try to get the cluster name from the connection string and deduce the project name you need in atlas-list-projects; if you are not sure, then ask the user for cluster name and project id.
    • Use slowQueryLogs to fetch slow query logs from the past 24 hours
    • Use suggestedIndexes
    • Use dropIndexSuggestions
    • Use schemaSuggestions
  2. Diagnose and Recommend: Based on slow query logs and performance advisor advice, you can create the compound index {status: 1, region: 1, date: -1} on the db.orders collection to optimize queries such as find({status: 'shipped', region: 'US'}).sort({date: -1})

Examine all performance advisor output as well as slow query logs. Provide information on what is being improved and why, and focus on suggestions that have the potential for greatest impact (e.g., indexes that affect the most queries, or queries that have the worst performance).

Load references

Before beginning diagnosis and recommendation, load reference files.

Always load:

  • references/core-indexing-principles.md
  • references/antipattern-examples.md

Conditionally load these files:

  • If diagnosing aggregation pipelines → references/aggregation-optimization.md
  • If diagnosing queries that change docs such as replaceOne, findOneAndUpdate, etc. → references/update-query-examples.md for oplog-efficient updates and common update anti-patterns

Output

  • Keep answers short and clear: a few sentences on index and optimization suggestions, and reasoning behind them (e.g. general indexing principles, observing slow query logs in the cluster, or seeing advice in Performance Advisor)
  • Focus on highest impact indexes or optimizations - if you've omitted some optimizations let the user know and present them if asked.
  • Do not use strong language, such as saying “You should create these indexes and they will definitely improve application performance” - Explain they are suggestions for certain queries, and give the reasoning behind them.
  • Consider how many indexes already exist on the collection (if known) - there shouldn’t generally be more than 20
  • Suggest removing indexes only if the suggestion comes from Atlas Performance Advisor
  • Do not create indexes directly via MCP unless the user gives approval

© mongodb, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in skills/mongodb-query-optimizer of mongodb/agent-skills.

  • SKILL.md
  • references/aggregation-optimization.md
  • references/antipattern-examples.md
  • references/core-indexing-principles.md
  • references/update-query-examples.md

Open the folder on GitHubat commit 18b014e

Used in 2 other repositories

We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in mongodb/agent-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Mongodb Query Optimizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Works with

Categories

Questions about Mongodb Query Optimizer

What does Mongodb Query Optimizer do?

Help with MongoDB query optimization and indexing. An agent skill from mongodb/agent-skills. Mongodb Query Optimizer is an agent skill from mongodb/agent-skills, published by the product's own GitHub organization. Help with MongoDB query optimization and indexing.

When should I use Mongodb Query Optimizer?

Mongodb Query Optimizer fits situations like: asks for optimization; performance: How do I optimize this query?; how do I index this?; why is this query slow?.

How do I install Mongodb Query Optimizer in Claude Code?

Run `npx skills add mongodb/agent-skills --skill mongodb-query-optimizer -a claude-code`. Or copy the skill folder (skills/mongodb-query-optimizer in mongodb/agent-skills) into .claude/skills/mongodb-query-optimizer in your project. Claude Code loads it when a task matches its description.

How do I install Mongodb Query Optimizer in Codex?

Run `npx skills add mongodb/agent-skills --skill mongodb-query-optimizer -a codex`. Or copy the skill folder (skills/mongodb-query-optimizer in mongodb/agent-skills) into .agents/skills/mongodb-query-optimizer in your project. Codex loads it when a task matches its description.

Can I use Mongodb Query Optimizer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mongodb/agent-skills --skill mongodb-query-optimizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mongodb-query-optimizer, .gemini/skills/mongodb-query-optimizer, .github/skills/mongodb-query-optimizer and .opencode/skills/mongodb-query-optimizer in your project.

What does Mongodb Query Optimizer need to run?

SKILL.md names no scripts, command-line tools or credentials: Mongodb Query Optimizer is instructions for the agent only. Compatibility (from SKILL.md): Best with MongoDB MCP server. Uses collection-indexes and explain when the connection string works; uses Atlas Performance Advisor when Atlas API is configured. Without either, suggest indexes from query shape only. User creates indexes in Atlas or migrations unless tooling allows otherwise..

Does Mongodb Query Optimizer access the network?

SKILL.md names 1 domain. As links in the text: mongodb.com. This is read from the text; nothing was executed.

Is Mongodb Query Optimizer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Mongodb Query Optimizer use?

Mongodb Query Optimizer is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mongodb Query Optimizer use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.7k tokens, read only when the agent opens those files.

What are the alternatives to Mongodb Query Optimizer?

Skills that share tags, products or a category with Mongodb Query Optimizer: Mindsdb MCP Skill (LeoYeAI/openclaw-master-skills, 2.2k stars), Pytorch Clickhouse (pytorch/test-infra, 113 stars), DB Sculptor (EliasOulkadi/shokunin, 114 stars) and Database Domain Specialist (modu-ai/moai-adk, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mongodb Query Optimizer?

mongodb (a GitHub organization, an official publisher) maintains it in mongodb/agent-skills, which has 190 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 6, 2026.

Source: mongodb/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.