mongodb-search-and-ai
mongodb search and ai
What it does
- Use list-databases and list-collections to understand available data
- If the user mentions a collection, use collection-schema to inspect field structure
- Use collection-indexes to see existing indexes
- Use atlas-inspect-cluster to determine the cluster's MongoDB version
- Ask clarifying questions about their needs
- Infer likely collection and fields from schema
- Confirm understanding before proceeding
Requirements & configuration
- 3. Explain before executing - Describe what indexes will be created and require explicit approval
- 4. Optimize for the use case - Different use cases require different index configurations and query patterns
- Ask clarifying questions about their needs
- Do they need exact matching, fuzzy matching, or semantic similarity?
- Do they need filters (price ranges, categories, dates)?
Derived from the skill's own SKILL.md documentation · extracted 2026-07-23
mongodb-search-and-ai FAQ
What does the mongodb-search-and-ai skill do?
| Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server. Use list-databases and list-collections to understand available data If the user mentions a collection, use collection-schema to inspect field structure
What does mongodb-search-and-ai require?
3. Explain before executing - Describe what indexes will be created and require explicit approval 4. Optimize for the use case - Different use cases require different index configurations and query patterns Ask clarifying questions about their needs Do they need exact matching, fuzzy matching, or semantic similarity? Do they need filters (price ranges, categories, dates)?
How do I install mongodb-search-and-ai?
Run: npx -y skills add https://github.com/mongodb/agent-skills --skill mongodb-search-and-ai --agent claude-code — the source lives at github.com/mongodb/agent-skills.
Maintain mongodb-search-and-ai?
Let people know it's listed here — add the badge (live metrics, light/dark aware) or a plain link to your README or docs.
[mongodb-search-and-ai on getagentictools](https://getagentictools.com/skills/mongodb-agent-skills-mongodb-search-and-ai?ref=badge) npx agentictools info skills/mongodb-agent-skills-mongodb-search-and-ai The second line is the CLI lookup for this page — handy in READMEs and docs.
