Databox MCP

Chat with your data. Anywhere.

Databox MCP is a Model Context Protocol server that connects your business data to AI assistants. Ask questions about your metrics in plain English—no SQL, no dashboard building, no data exports.

Overview

Databox MCP enables AI tools like Claude, Cursor, n8n, and Gemini CLI to access and analyze your Databox data conversationally. It transforms how you interact with business metrics—instead of navigating dashboards, you simply ask questions and get instant answers.

Key Benefits:

• Query your data using natural language
• Works with 130+ existing Databox integrations
• No additional cost for Databox users
• Setup in under 60 seconds

Supported AI Clients

Client	Status
Claude Desktop	Supported
Claude Web	Supported
Cursor	Supported
n8n	Supported
Gemini CLI	Supported
Any MCP-compatible tool	Supported

Quick Setup

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "databox": {
      "type": "http",
      "url": "https://mcp.databox.com/mcp"
    }
  }
}

Claude Web / Claude Desktop App

1. Go to Settings → Connectors
2. Click Add Custom Connector
3. Enter the remote server URL: https://mcp.databox.com/mcp
4. Complete the authorization flow

Cursor

Add the Databox MCP server in Cursor's MCP settings with the URL https://mcp.databox.com/mcp.

n8n

Use an HTTP Request node pointing to https://mcp.databox.com/mcp and build your workflows from there.

Available Tools

Databox MCP exposes 15 tools for interacting with your data:

Account Management

list_accounts

List all Databox accounts accessible to the authenticated user.

No parameters.

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Data Sources

list_data_sources

List all data sources for a specific account.

Parameter	Type	Required	Description
account_id	string	Yes	Unique identifier of the account

create_data_source

Create a new data source container for organizing datasets.

Parameter	Type	Required	Description
name	string	Yes	Human-readable name for the data source
account_id	string	No	Target account ID. Defaults to the account associated with the API key

delete_data_source

Permanently remove a data source and all its associated datasets. Cannot be undone.

Parameter	Type	Required	Description
data_source_id	string	Yes	Unique identifier of the data source to delete

list_data_source_datasets

List all datasets belonging to a specific data source.

Parameter	Type	Required	Description
data_source_id	string	Yes	Unique identifier of the data source

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Datasets

create_dataset

Create a new dataset within a data source, with an optional schema.

Parameter	Type	Required	Description
data_source_id	string	Yes	ID of the parent data source
name	string	Yes	Human-readable name for the dataset
columns	string (JSON)	No	Column schema as a JSON array. Each column has name (string) and data_type ("string", "number", or "datetime")
primary_keys	string (JSON)	No	JSON array of column names to use as composite key (e.g. '["id"]')

ingest_data

Push data records into an existing dataset.

Parameter	Type	Required	Description
dataset_id	string	Yes	Unique identifier of the target dataset (UUID)
data	string (JSON)	Yes	JSON array of records, each an object with column names as keys

get_dataset_ingestions

Get ingestion history for a specific dataset.

Parameter	Type	Required	Description
dataset_id	string	Yes	Unique identifier of the dataset (UUID)

get_ingestion

Get detailed information for a specific ingestion event, including record counts and dataset metrics.

Parameter	Type	Required	Description
dataset_id	string	Yes	Unique identifier of the dataset (UUID)
ingestion_id	string	Yes	Unique identifier of the ingestion event (UUID)

delete_dataset

Permanently remove a dataset and all its data. Cannot be undone.

Parameter	Type	Required	Description
dataset_id	string	Yes	Unique identifier of the dataset to delete (UUID)

list_merged_datasets

List all merged datasets for a specific account. Merged datasets combine data from multiple sources.

Parameter	Type	Required	Description
account_id	string	Yes	Unique identifier of the account

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Metrics

list_metrics

List all metrics available for a data source (Google Analytics, Stripe, etc.).

Parameter	Type	Required	Description
data_source_id	integer	Yes	Data source ID to list metrics for

load_metric_data

Load data for a metric over a date range with optional dimensions and time-series granulation.

Parameter	Type	Required	Description
data_source_id	integer	Yes	Data source ID for the metric
metric_key	string	Yes	Short metric key (e.g. "GoogleAnalytics4@sessions")
start_date	string	Yes	Start date in YYYY-MM-DD format
end_date	string	Yes	End date in YYYY-MM-DD format
dimension	string	No	Dimension key to break down by (e.g. "source")
granulation_time_unit	integer	No	Time unit for time series: 1=hour, 2=day, 3=week, 4=month
is_whole_range	boolean	No	If true (default), returns single aggregated value. Automatically set to false when granulation_time_unit is provided
record_limit	integer	No	Maximum number of dimension value records to return

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AI-Powered Analysis

ask_genie

Query your data using natural language, powered by Genie AI. Genie executes actual queries against your data and returns calculated results, not LLM approximations. Supports conversation threading for follow-up questions.

Parameter	Type	Required	Description
dataset_id	string	Yes	Unique identifier of the dataset to analyze (UUID)
question	string	Yes	Natural language question about the data
thread_id	string	No	Thread ID from a previous response to continue the conversation

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Utilities

get_current_datetime

Get the current date and time. Use this to resolve relative date expressions like "last month" or "yesterday" before calling other tools.

Parameter	Type	Required	Description
timezone	string	No	Timezone name (e.g. "UTC", "America/New_York"). Defaults to UTC

How It Works

Databox MCP uses a three-layer architecture to ensure accurate, reliable answers:

1. Data Platform – Structured datasets with schemas, types, and validation
2. Analytic Query Engine – Executes actual queries (aggregations, joins, filters)
3. Semantic Layer – Understands business definitions and metric relationships

The AI never touches your calculations directly. It formulates queries, the engine executes them, and the AI summarizes the results. This means you get real calculations, not statistical approximations.

Authentication

Databox MCP uses secure authentication:

• OAuth 2.0 for user authorization
• JWT token validation for secure sessions
• API key authentication for programmatic access

Your data remains within your Databox account with existing governance standards. AI access is limited to explicitly granted data permissions.

Security

• Encrypted connections (HTTPS)
• Scope-based authorization
• Audit trails and ingestion history
• No vendor lock-in (universal MCP standard)
• Data isolation per account

Use Cases

Ad-hoc Analysis

"What was our conversion rate last week compared to the previous week?"

Cross-source Insights

"Calculate ROAS by combining ad spend from Google Ads with revenue from Stripe"

Trend Detection

"Which product category has the highest refund rate this quarter?"

Automated Alerts

"Alert me if the 3-day conversion rate drops below 2%"

Data Cleanup

Push messy CSV exports and let Databox normalize dates, formats, and schemas automatically

Direct Metric Queries

"Show me Google Analytics sessions for the last 30 days broken down by traffic source"

Time-Series Analysis

"Load daily page views for January with weekly aggregation"

Dimension Breakdowns

"What are the top 10 countries by revenue from Stripe?"

Resources

• Databox MCP Landing Page
• Blog: Chat with Your Data Anywhere
• Model Context Protocol Specification
• Databox Help Center

Support

For questions and support:

• Visit the Databox Help Center
• Contact support@databox.com

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