General guidance for developing in Databricks, a cloud platform for data processing and analytics. It covers PySpark, notebooks, Asset Bundles, pipelines, and Unity Catalog names such as catalog.schema.table.

Use it when writing Databricks PySpark or notebook code, creating pipelines, organising Asset Bundles, or referring to Unity Catalog data.

It keeps coding advice aligned with the conventions and assumptions of a Databricks workspace.

general — Cursor rule by revodatanl

Install

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.

agentmods

npx agentmods add rules/revodatanl/databricks-mcp-server/general

Clone the repo

git clone --depth 1 https://github.com/revodatanl/databricks-mcp-server

Made for: Cursor.

Per session 968 This file is loaded in full into every session.

When invoked 968 The same file — it is already loaded in full.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00968	$0.00968
Opus 5	$0.00484	$0.00484
Sonnet 5	$0.00194	$0.00194
Haiku 4.5	$0.00097	$0.00097

Measured 2d ago against content hash d2df1354a49b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

general 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 2d 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.

rules/.cursor/general.mdc · 94 lines

How it starts

The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.

General Databricks Development Rules

Core Principles

You are an expert in Databricks workflows and best practices
Always begin with a simple solution; expand only if the user requests more detail
Strictly follow the user's question—do not provide more than what is asked
Prefer Databricks PySpark code over SQL when providing code examples
If you are unsure about the user's intent or the next step, ask for clarification
Assume all work is within a Databricks Asset Bundle structure
Assume the user is working in a Databricks workspace environment
"Jobs" refer to Databricks workflows
Always use databricks notebook format in .py files, unless working in the src folder
You may assume there is a spark session available in the spark variable. There is no need to configure a session, databricks connect handles this

Pipeline Creation Workflow

When asked to create a pipeline, provide only the Python code first, then ask if the user wants the pipeline configuration
Define all pipeline configurations in YAML files located in the resources folder

Unity Catalog & Namespacing

Unity Catalog uses a three-level namespace: catalog.schema.table
The full name may not always be known by the user
Databricks tables use catalog.schema.table naming

Python Notebooks in Databricks

When creating a Python notebook for Databricks, use a .py file and begin with # Databricks notebook source
Separate cells in a Python notebook using the line # COMMAND ----------

Databricks Library Resolution

Before adding or updating any Databricks dependency, call resolve-library-id to fetch the latest identifier/version

Data Discovery & Validation

When asked about a table or Databricks entity, use databricks_mcp_server
Prefer answers already in chat history
Verify catalogs, schemas, tables, and columns with revodata_databricks_mcp before writing or changing code/SQL
Happy-path workflow (in order when relevant):
1. get-all-catalogs-schemas-tables → list Unity Catalog objects
2. (Optional) Filter and record chosen catalog.schema.table
3. get-table-details with full_table_names=["catalog.schema.table", ...] → confirm columns, types, partitioning, comments

Read the full file on GitHub · 94 lines

Changes

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.

2d ago First seen · 94 lines · 968 tokens per session scan A d2df1354a49b

Subscribe to this mod's changes

general is a cursor rule published in the GitHub repository revodatanl/databricks-mcp-server (7 stars, last pushed 1mo ago), licensed MIT. It adds 968 tokens to every session, about $0.0048 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 2026-08-31.

Analytics and metrics Data and AI Game development

architecture

Architectural and cross-package changes — dependency direction, editor/runtime boundaries, scene schema, persistence, renderer packages. Use when changing package boundaries, serialized formats, or multiple layers. Do not apply to localized UI, styling, or test-only edits.

andvolodko/html5-game-editor · 49 tokens

general