awesome-cursor-rules-mdc is a generator that creates Cursor MDC rule files from structured library information, using semantic search and language models to gather and organize guidance. Developers use it to produce reusable rules for libraries in Cursor, and the catalogue includes 200 of those rules.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/sanjeed5/awesome-cursor-rules-mdcWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/mongodb)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/mongodb"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/mongodb.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.03251 | $0.03251 |
| Opus 5 | $0.01625 | $0.01625 |
| Sonnet 5 | $0.00650 | $0.00650 |
| Haiku 4.5 | $0.00325 | $0.00325 |
Grade A, and why
mongodb scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mongodb Best Practices
MongoDB's flexible schema is a superpower, but with great power comes great responsibility. This guide cuts through the noise to give you the definitive, opinionated best practices for building robust, high-performance applications with MongoDB. Follow these rules to avoid common pitfalls and leverage MongoDB effectively.
1. Data Modeling: Store Data Accessed Together, Together
The cardinal rule: embed data that is accessed together within a single document. This minimizes joins and maximizes read performance. Use referencing for many-to-many relationships or when embedded data would exceed the 16MB document limit or grow unboundedly.
1.1. Embed for Read Performance
✅ GOOD: Embed tightly coupled, frequently accessed data.
// User document with embedded preferences
{
"_id": ObjectId("656e2c0e8a7b9c1d2e3f4a5b"),
"username": "johndoe",
"email": "[email protected]",
"preferences": {
"theme": "dark",
"notifications": {
"email": true,
"sms": false
}
}
}
❌ BAD: Separating tightly coupled data into multiple collections, forcing $lookup for common reads.
// User document
{ "_id": ObjectId("656e2c0e8a7b9c1d2e3f4a5b"), "username": "johndoe", "email": "[email protected]" }
// UserPreferences document (separate collection)
{ "_id": ObjectId("656e2c0e8a7b9c1d2e3f4a5c"), "userId": ObjectId("656e2c0e8a7b9c1d2e3f4a5b"), "theme": "dark", "notifications": { "email": true, "sms": false } }
// Requires a join to get user with preferences.
1.2. Reference for Unbounded Growth or Many-to-Many
Use referencing when a sub-document array could grow indefinitely (e.g., comments on a blog post) or when documents are frequently updated independently.
✅ GOOD: Reference comments from a blog post.
// Post document
{
"_id": ObjectId("post123"),
"title": "My Awesome Post",
"content": "...",
"authorId": ObjectId("user456"),
"commentCount": 5 // Denormalized for quick access
}
// Comment document (separate collection)
{
"_id": ObjectId("comment789"),
"postId": ObjectId("post123"),
"authorId": ObjectId("user987"),
"text": "Great post!",
"createdAt": ISODate("2025-01-01T10:00:00Z")
}
❌ BAD: Embedding an unbounded array of comments within the post document. This risks exceeding the 16MB document limit and makes updates inefficient.
// Post document with embedded comments
{
"_id": ObjectId("post123"),
"title": "My Awesome Post",
"content": "...",
"authorId": ObjectId("user456"),
"comments": [ // This array can grow indefinitely!
{"authorId": ObjectId("user987"), "text": "Great post!", "createdAt": ISODate("2025-01-01T10:00:00Z")},
{"authorId": ObjectId("user111"), "text": "I agree!", "createdAt": ISODate("2025-01-01T10:05:00Z")}
]
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 339 lines · 3,251 tokens per session scan A 350b1fd6f027
mongodb is a cursor rule published in the GitHub repository sanjeed5/awesome-cursor-rules-mdc (3,571 stars, last pushed 3mo ago), licensed CC0-1.0. It adds 3,251 tokens to every session, about $0.0163 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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cpp-api-patterns
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fastapi_endpoint_tracking_with_mongodb
Complete guide for implementing FastAPI endpoint tracking with MongoDB storage and analytics.
mongodb
Development guidelines for MongoDB, a database that stores data as flexible documents, with examples using Mongoose, a Node.js library for defining and querying them. They cover schemas, aggregation, indexes, transactions, and document design.