惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

有赞技术团队
有赞技术团队
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
P
Palo Alto Networks Blog
C
Cisco Blogs
The Hacker News
The Hacker News
T
Threatpost
S
Schneier on Security
K
Kaspersky official blog
Spread Privacy
Spread Privacy
博客园_首页
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
NISL@THU
NISL@THU
量子位
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Google DeepMind News
Google DeepMind News
Security Latest
Security Latest
博客园 - 司徒正美
云风的 BLOG
云风的 BLOG
博客园 - 叶小钗
H
Hackread – Cybersecurity News, Data Breaches, AI and More
N
News and Events Feed by Topic
爱范儿
爱范儿
P
Proofpoint News Feed
C
CERT Recently Published Vulnerability Notes
Project Zero
Project Zero
Application and Cybersecurity Blog
Application and Cybersecurity Blog
Cisco Talos Blog
Cisco Talos Blog
GbyAI
GbyAI
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
Apple Machine Learning Research
Apple Machine Learning Research
T
Tenable Blog
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
V
Vulnerabilities – Threatpost
Forbes - Security
Forbes - Security
博客园 - 三生石上(FineUI控件)
C
Cyber Attacks, Cyber Crime and Cyber Security
N
News and Events Feed by Topic
V
V2EX
Webroot Blog
Webroot Blog
The Register - Security
The Register - Security
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
阮一峰的网络日志
阮一峰的网络日志
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Blog — PlanetScale
Blog — PlanetScale
M
MIT News - Artificial intelligence
Scott Helme
Scott Helme
Simon Willison's Weblog
Simon Willison's Weblog
L
LangChain Blog
W
WeLiveSecurity
Cloudbric
Cloudbric

MongoDB | Blog

10 Years of MongoDB Atlas: Built for what’s Next Build Trust in Agentic AI: From POC to Production Production-Ready Agents Need A Production-Ready Data Platform Agentic Supplier Management with MongoDB Atlas, Voyage AI, and Multi-Modal Search Fighting Tool Sprawl: The Case for AI Tool Registries AI Is Changing What Customers Need From a Database. MongoDB 8.3 Is Built for It New Research Reveals Overcoming Legacy Tech Issues Key to AI Success MongoDB Predictive Auto-Scaling: An Experiment Enhance Your In-IDE Data Browsing Experience With MongoDB Observability and OpenTelemetry: Introducing MongoDB Atlas Log Integration Towards Model-based Verification of a Key-Value Storage Engine Inside MongoDB Dublin: The Heart of Our International Growth Innovating with MongoDB | Customer Successes, February 2026 Building a Movie Recommendation Engine with Hugging Face and Voyage AI Edge AI Made Easy: MongoDB and ObjectBox Data Synchronization MongoDB.local San Francisco 2026: Ship Production AI, Faster Vision RAG: Enabling Search on Any Documents That’s a Wrap! MongoDB’s 2025 in Review & 2026 Predictions Token-count-based Batching: Faster, Cheaper Embedding Inference for Queries MongoDB Announces Leadership Transition Cars24 Improves Search For 300 Million Users With MongoDB Atlas The Cost of Not Knowing MongoDB, Part 3: appV6R0 to appV6R4 The 10 Skills I Was Missing as a MongoDB User Innovating with MongoDB | Customer Successes, October 2025 Smarter AI Search, Powered by MongoDB Atlas and Pureinsights Charting a New Course for SaaS Security: Why MongoDB Helped Build the SSCF Top Considerations When Choosing a Hybrid Search Solution Endian Communication Systems and Information Exchange in Bytes MongoDB SQL Interface: Now Available for Enterprise Advanced From Niche NoSQL to Enterprise Powerhouse: The Story of MongoDB's Evolution Carrying Complexity, Delivering Agility MongoDB is a Glassdoor Best-Led Company of 2025 Build AI Agents Worth Keeping: The Canvas Framework Simplify AI-Driven Data Connectivity With MongoDB and MCP Toolbox MongoDB Community Edition to Atlas: A Migration Masterclass With BharatPE Modernizing Core Insurance Systems: Breaking the Batch Bottleneck MongoDB.local NYC 2025:定义 AI 时代的理想数据库 MongoDB.local NYC 2025: Defining the Ideal Database for the AI Era MongoDB.local NYC 2025: Definiendo la base de datos ideal para la era de la IA MongoDB.local NYC 2025 : définir la base de données idéale à l'ère de l'IA MongoDB.local NYC 2025: Definindo o Banco de Dados Ideal para a Era da IA MongoDB.local NYC 2025: AI 시대를 위한 이상적인 데이터베이스 정의 MongoDB.local NYC 2025: Definition der idealen Datenbank für das KI-Zeitalter MongoDB.local NYC 2025: Definire il database ideale per l'era dell'AI Hommage à l’excellence : MongoDB Global Partner Awards 2025 Wir feiern Spitzenleistungen: MongoDB Global Partner Awards 2025 Celebrating Excellence: MongoDB Global Partner Awards 2025 庆祝卓越:MongoDB 全球合作伙伴奖 2025 Celebrando la Excelencia: Premios Globales de Emparejar de MongoDB 2025 Começando a destacar a excelência: MongoDB GlobalPartner Services 2025 Celebrare l'eccellenza: MongoDB Global Partner Awards 2025 우수성을 기념하기: 2025년 MongoDB 글로벌 파트너 어워드 The Future of AI Software Development is Agentic MongoDB Queryable Encryption Expands Search Power Supercharge Self-Managed Apps With Search and Vector Search Capabilities Potencie las aplicaciones autogestionadas con capacidades de búsqueda y búsqueda vectorial
Introducing MongoDB Agent Skills and Plugins for Coding Agents
Thibaut Gourdel, Eliza Spang · 2026-03-31 · via MongoDB | Blog

Software engineering is evolving into agentic engineering. According to the Stack Overflow Developer Survey 2025, 84% of respondents use or plan to use AI tools in their development, up from 76% the previous year. At this rate, the tooling needs to keep pace. 

Last year, we introduced the MongoDB MCP Server to give agents the connectivity they need to interact with MongoDB, helping them generate context-aware code. But connectivity was only the start. Agents are generalists by design, and they don't inherently know the best practices and design patterns that real-world production systems demand.

Today, we're addressing this by introducing official MongoDB Agent Skills: structured instructions, best practices, and resources that agents can discover and apply to generate more reliable code across the full development lifecycle, from schema design and performance optimization to implementing advanced capabilities like AI retrieval. 

To bring this directly into the tools you use, we're also launching plugins for Claude Code, Cursor, Gemini CLI, and VS Code, combining the MongoDB MCP Server and Agent Skills in a single, ready-to-use package.

Turning coding agents into MongoDB experts

Coding agents are great at producing working code, but they still make common mistakes in production systems, often defaulting to relational thinking that doesn't translate well to MongoDB, such as:

  • Over-normalizing schemas, ignoring MongoDB's document-oriented strengths.

  • Underusing compound indexes, causing performance bottlenecks at scale.

  • Misusing indexes and search indexes, overlooking the consistency trade-off for high-performance full-text search.

Because these pitfalls mirror common human errors, they are naturally reflected in agent outputs. MongoDB Agent Skills address this by providing expert guidance to agents, like schema design heuristics, indexing strategies, query patterns, and operational safeguards, enabling agents to ship more reliable, more consistent code faster.

Agent Skills were introduced by Anthropic as an open standard and have since been adopted by the leading AI development tools, including Claude Code, Cursor, Codex, and more.

This initial release covers the full application development lifecycle on MongoDB, from connection management and schema design to guidance on implementing advanced capabilities. We will continue to update and expand our skills library based on user needs.

Figure 1. MongoDB Agent Skills.

Scaling agentic engineering with MongoDB

As organizations embrace agentic software engineering, existing processes and workflows must be reimagined. The MongoDB MCP Server and MongoDB Agent Skills are built for this shift and work best together, giving builders and agents the tools to move fast without sacrificing guardrails or control.

The MongoDB MCP Server serves as the connectivity layer for your MongoDB deployments. It manages authentication and defines exactly what agents can access and do. Combined with MongoDB’s native authorization, it ensures agents operate with only the permissions they need, while giving teams governance through configurable controls like disabling specific tools.

Agent Skills ensure agents follow best practices from the start, reducing architectural risk, accelerating implementation, and raising the baseline quality of every agent-generated code. 

While some skills can be used independently, others work in conjunction with the MongoDB MCP Server for workflows that require it. To simplify setup, the MCP Server and skills are now packaged together as plugins and extensions for Claude Code, Cursor, Gemini CLI, and VS Code, bringing these capabilities directly into your preferred tools.

Figure 2. MongoDB for Claude plugin in action.

We also encourage you to build your own skills as your agentic workflows mature. Whether enforcing internal naming conventions, custom data modeling patterns, or team-specific workflows, skills give you a practical way to codify institutional knowledge and ensure every agent and every developer works from the same playbook.

How to get started

Whether you’re using Claude Code, Cursor, Gemini CLI, or other AI development tools, you can install the MongoDB MCP Server and Agent Skills in seconds.

For example, in Claude Code, install the plugin that bundles both:

For Cursor, Gemini CLI, and VS Code extensions, refer to their respective documentation.

You can also install the skills for most coding agents using the Vercel Skills CLI (requires Node.js):

If you prefer, you can manually clone the GitHub repository and copy the skills into the appropriate folder for your agent.

Similarly, to install the MongoDB MCP Server, use the following command:

Agentic engineering is changing how teams work, and it is changing fast. Agents need the context and guidance to meet the standards of real-world production applications. With the official MongoDB Agent Skills and plugins, builders can move faster with confidence, and organizations can adopt coding agents knowing that MongoDB best practices are embedded directly into every workflow.