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/revodatanl/databricks-mcp-serverWrote 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/revodatanl/databricks-mcp-server/general)<a href="https://agentmods.dev/rules/revodatanl/databricks-mcp-server/general"><img src="https://agentmods.dev/badge/rules/revodatanl/databricks-mcp-server/general/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/revodatanl/databricks-mcp-server/general"><img src="https://agentmods.dev/badge/rules/revodatanl/databricks-mcp-server/general.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00968 | $0.00968 |
| Opus 5.5 | $0.00387 | $0.00387 |
| Sonnet 5.5 | $0.00194 | $0.00194 |
| Haiku 4.5 | $0.00097 | $0.00097 |
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 2mo 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 — 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
.pyfiles, unless working in thesrcfolder - You may assume there is a spark session available in the
sparkvariable. 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
resourcesfolder
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.tablenaming
Python Notebooks in Databricks
- When creating a Python notebook for Databricks, use a
.pyfile 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-idto 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_mcpbefore writing or changing code/SQL - Happy-path workflow (in order when relevant):
get-all-catalogs-schemas-tables→ list Unity Catalog objects- (Optional) Filter and record chosen
catalog.schema.table get-table-detailswithfull_table_names=["catalog.schema.table", ...]→ confirm columns, types, partitioning, comments
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.
- 2mo ago First seen · 94 lines · 968 tokens per session scan A d2df1354a49b
general is a cursor rule published in the GitHub repository revodatanl/databricks-mcp-server (7 stars, last pushed 5d ago), licensed MIT. It adds 968 tokens to every session, about $0.0039 per session on Opus 5.5. 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.
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