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laraib-sidd

bricks-and-context

by laraib-sidd
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Python
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🧱 Bricks and Context

Production-grade Model Context Protocol (MCP) server for Databricks

CI Python 3.10+ License: MIT MCP

SQL Warehouses · Jobs API · Multi-Workspace · Built for AI Agents

✨ What is this?

Bricks and Context lets AI assistants (Cursor, Claude Desktop, etc.) talk directly to your Databricks workspaces through the Model Context Protocol.

Think of it as a bridge: your AI asks questions, this server translates them into Databricks API calls, and returns structured, AI-friendly responses.

Why use this?

Pain Point

How we solve it

AI gets overwhelmed by huge query results

Bounded outputs — configurable row/byte/cell limits

Flaky connections cause random failures

Retries + circuit breakers — automatic fault tolerance

Managing multiple environments is tedious

Multi-workspace — switch between dev/prod with one parameter

Raw API responses confuse AI models

Markdown tables — structured, LLM-optimized output

Related MCP server: Databricks MCP Server Template

🔧 Available Tools

Tool

What it does

execute_sql_query

Run SQL with bounded, AI-safe output

discover_schemas

List all schemas in the workspace

discover_tables

List tables in a schema with metadata

describe_table

Get column types, nullability, structure

get_table_sample

Preview rows for data exploration

connection_health

Verify Databricks connectivity

Tool

What it does

list_jobs

List jobs with optional name filtering

get_job_details

Full job config: schedule, cluster, tasks

get_job_runs

Run history with state and duration

trigger_job

Start a job with optional parameters

cancel_job_run

Stop a running job

get_job_run_output

Retrieve logs, errors, notebook output

Tool

What it does

cache_stats

Hit rates, memory usage, category breakdown

performance_stats

Operation latencies, error rates, health

🚀 Quick Start

1. Clone & Install

git clone https://github.com/laraib-sidd/bricks-and-context.git
cd bricks-and-context
uv sync  # or: pip install -e .

2. Configure Workspaces

Copy the template and add your credentials:

cp auth.template.yaml auth.yaml

Edit auth.yaml:

default_workspace: dev

workspaces:
  - name: dev
    host: your-dev.cloud.databricks.com
    token: dapi...
    http_path: /sql/1.0/warehouses/...

  - name: prod
    host: your-prod.cloud.databricks.com
    token: dapi...
    http_path: /sql/1.0/warehouses/...

💡 auth.yaml is gitignored. Your secrets stay local.

3. Run

python run_mcp_server.py

🎯 Cursor Integration

Cursor uses stdio transport and doesn't inherit your shell environment. You need explicit paths.

Step 1: Ensure dependencies are installed

cd /path/to/bricks-and-context
uv sync

Step 2: Open MCP settings in Cursor

Cmd+Shift+P → "Open MCP Settings" → Opens ~/.cursor/mcp.json

Step 3: Add this configuration

Using uv run (recommended):

{
  "mcpServers": {
    "databricks": {
      "command": "uv",
      "args": [
        "--directory", "/path/to/bricks-and-context",
        "run", "python", "run_mcp_server.py"
      ],
      "env": {
        "MCP_AUTH_PATH": "/path/to/bricks-and-context/auth.yaml",
        "MCP_CONFIG_PATH": "/path/to/bricks-and-context/config.json"
      }
    }
  }
}

Or using venv directly:

{
  "mcpServers": {
    "databricks": {
      "command": "/path/to/bricks-and-context/.venv/bin/python",
      "args": ["/path/to/bricks-and-context/run_mcp_server.py"],
      "env": {
        "MCP_AUTH_PATH": "/path/to/bricks-and-context/auth.yaml",
        "MCP_CONFIG_PATH": "/path/to/bricks-and-context/config.json"
      }
    }
  }
}

Step 4: Restart Cursor

Reload the window to activate the MCP server.

Test it

Ask your AI:

  • "List my Databricks jobs"

  • "Run SELECT 1 on Databricks"

  • "Describe the table catalog.schema.my_table"

🌐 Multi-Workspace

Define multiple workspaces in auth.yaml, then select per-call:

execute_sql_query(sql="SELECT 1", workspace="prod")
list_jobs(limit=10, workspace="dev")

When workspace is omitted, the server uses default_workspace.

⚙️ Configuration

config.json — Tunable settings (committed)

Setting

Default

Description

max_connections

10

Connection pool size

max_result_rows

200

Max rows returned per query

max_result_bytes

262144

Max response size (256KB)

max_cell_chars

200

Truncate long cell values

allow_write_queries

false

Enable INSERT/UPDATE/DELETE

enable_sql_retries

true

Retry transient SQL failures

enable_query_cache

false

Cache repeated queries

query_cache_ttl_seconds

300

Cache TTL

databricks_api_timeout_seconds

30

Jobs API timeout

Any setting can be overridden via environment variable (uppercase, e.g., MAX_RESULT_ROWS=500).

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                   MCP Client (Cursor / Claude)                  │
└─────────────────────────────────────────────────────────────────┘
                                │ stdio
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│                     FastMCP Server                              │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────────┐  │
│  │ SQL Tools   │  │ Job Tools   │  │ Observability           │  │
│  └──────┬──────┘  └──────┬──────┘  └───────────┬─────────────┘  │
└─────────┼────────────────┼─────────────────────┼────────────────┘
          │                │                     │
          ▼                ▼                     ▼
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────────┐
│ Connection Pool  │ │  Job Manager     │ │ Cache / Perf Monitor │
│  (SQL Connector) │ │  (REST API 2.1)  │ │                      │
└────────┬─────────┘ └────────┬─────────┘ └──────────────────────┘
         │                    │
         └────────┬───────────┘
                  ▼
┌─────────────────────────────────────────────────────────────────┐
│                   Databricks Workspace(s)                       │
│              SQL Warehouse        Jobs Service                  │
└─────────────────────────────────────────────────────────────────┘

🛡️ Reliability Features

Feature

Description

Bounded outputs

Rows, bytes, and cell-character limits prevent OOM

Connection pooling

Thread-safe with per-connection health validation

Retry with backoff

Exponential backoff + jitter for transient failures

Circuit breakers

Automatic fault isolation, prevents cascading failures

Query caching

Optional TTL-based caching for repeated queries

🧑‍💻 Development

uv sync --dev        # Install dev dependencies
uv run pytest        # Run tests
uv run black .       # Format code
uv run mypy src/     # Type check

📄 License

MIT — see LICENSE

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