Mongodb Search And Ai
by mongodb1e72df255e54Apache-2.0Listed Oct 8, 2026Updated Oct 8, 2026
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.
Add to a SourceWeft workspace
- Open the skill in your dashboard and add it to a workspace.
- Enable it for the chats that should use it.
This skill is instructions only: it ships no scripts to execute.
Add to SourceWeftYou will be asked to sign in first, then taken straight to this skill.
Ask your agent to install it
Paste this prompt into Claude Code, Codex, Cursor or another agent that can run commands — or into SourceWeft chat. The agent reads this skill's install guide, shows you its source, license and scripts, and installs it with the SourceWeft CLI once you agree.
Read https://sourceweft.com/skills/gh-mongodb-agent-skills-mongodb-search-and-ai/install.md and install the skill it describes. Before installing, show me its source, license and whether it ships scripts, and wait for my OK. Ask me before changing anything else on my machine.Install it yourself from a terminal
For Claude Code, Codex, Cursor and other local agents. The SourceWeft CLI fetches the skill from its source repository at the commit scanned here, and verifies every file against the hashes recorded when the skill was scanned. If anything differs, nothing is written.
npx @sourceweft/cli skills install gh-mongodb-agent-skills-mongodb-search-and-aiAdd --agent claude-code, codex, cursor or universal to choose which agent gets it (Claude Code by default).
Upstream installer — not verified by SourceWeft
The open-source skills installer fetches the same pinned commit, but does not check the files against the hashes SourceWeft recorded.
npx skills add https://github.com/mongodb/agent-skills/tree/1e72df255e54e81eff078054c2cfb2b5d8c13503/plugins/mongodb-atlas/skills/mongodb-search-and-aiSource and attribution
Source:mongodb/agent-skillsinplugins/mongodb-atlas/skills/mongodb-search-and-aiat commit1e72df2
License: Apache-2.0
Content belongs to its original authors. SourceWeft indexes it from a public repository.
More from mongodb/agent-skills
Mongodb Schema Design
mongodb
Guides MongoDB schema design, data modeling patterns, anti-patterns, and access-pattern analysis.
Mongodb Query Optimizer
mongodb
Guides MongoDB query and index optimization using explain plans, index inspection and Atlas performance advisor data.
Mongodb Natural Language Querying
mongodb
Generates read-only MongoDB find queries and aggregation pipelines from natural language using schema context.
Mongodb Connection
mongodb
Guides MongoDB client connection configuration, including pools, timeouts and troubleshooting, across driver languages.
Mongodb Atlas Stream Processing
mongodb
Guides building, operating, and debugging MongoDB Atlas Stream Processing pipelines through MongoDB MCP Server tools.
More in Data & Analytics
Spanner Basics
Guides Google Cloud Spanner administration, schema design, querying and performance diagnosis.
Gke Cost Analysis
Answers natural-language questions about GKE cluster and workload costs using BigQuery billing exports and live cluster metrics.
Datalineage Bigquery Asset Impact Analysis
Guides an agent through downstream impact (blast radius) analysis for a BigQuery table or view using Data Lineage.
Bigquery Troubleshooting
Diagnoses failing, slow, or unexpectedly expensive BigQuery jobs through structured root-cause workflows.
Bigquery Optimization
Guides BigQuery cost and performance optimization across capacity editions, storage layout, and SQL queries.
Bigquery Bigframes
Guides writing Python code with BigQuery DataFrames (BigFrames) for data processing, analysis, and machine learning.