Databox MCP connector

1. Install the SDK

   Node.js

   npm install @scalekit-sdk/node

   Python

   pip install scalekit

   Full SDK reference: Node.js | Python

2. Set your credentials

   Add your Scalekit credentials to your .env file. Find values in app.scalekit.com > Developers > API Credentials.

   .env

   SCALEKIT_ENVIRONMENT_URL=<your-environment-url>
   SCALEKIT_CLIENT_ID=<your-client-id>
   SCALEKIT_CLIENT_SECRET=<your-client-secret>

3. Authorize and make your first call

   Node.js

   quickstart.ts

   import { ScalekitClient } from '@scalekit-sdk/node'
   import 'dotenv/config'
   
   const scalekit = new ScalekitClient(
     process.env.SCALEKIT_ENV_URL,
     process.env.SCALEKIT_CLIENT_ID,
     process.env.SCALEKIT_CLIENT_SECRET,
   )
   const actions = scalekit.actions
   
   const connector = 'databoxmcp'
   const identifier = 'user_123'
   
   // Generate an authorization link for the user
   const { link } = await actions.getAuthorizationLink({ connectionName: connector, identifier })
   console.log('Authorize Databox MCP:', link)
   process.stdout.write('Press Enter after authorizing...')
   await new Promise(r => process.stdin.once('data', r))
   
   // Make your first call
   const result = await actions.executeTool({
     connector,
     identifier,
     toolName: 'databoxmcp_get_current_datetime',
     toolInput: {},
   })
   console.log(result)

   Python

   quickstart.py

   import os
   from scalekit.client import ScalekitClient
   from dotenv import load_dotenv
   load_dotenv()
   
   scalekit_client = ScalekitClient(
       env_url=os.getenv("SCALEKIT_ENV_URL"),
       client_id=os.getenv("SCALEKIT_CLIENT_ID"),
       client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"),
   )
   actions = scalekit_client.actions
   
   connection_name = "databoxmcp"
   identifier = "user_123"
   
   # Generate an authorization link for the user
   link_response = actions.get_authorization_link(
       connection_name=connection_name,
       identifier=identifier,
   )
   print("Authorize Databox MCP:", link_response.link)
   input("Press Enter after authorizing...")
   
   # Make your first call
   result = actions.execute_tool(
       tool_input={},
       tool_name="databoxmcp_get_current_datetime",
       connection_name=connection_name,
       identifier=identifier,
   )
   print(result)

What you can do

Connect this agent connector to let your agent:

• Data load metric, ingest — Retrieve data points for a Databox metric over a date range with optional time-series granulation and dimension breakdown
• List metrics, merged datasets, data sources — List all metrics available for a Databox data source, including metric keys, names, descriptions, and available dimensions
• Get ingestion, dataset ingestions, current datetime — Get detailed information for a specific ingestion event, including status, timestamps, dataset metrics, and per-record ingestion outcomes
• Delete dataset, data source — Permanently delete a dataset and all its data from Databox
• Create dataset, data source — Create a structured dataset within a Databox data source, optionally defining a column schema and primary keys for tabular data storage
• Genie ask — Ask Genie, the Databox AI data analyst, to explore and analyze a dataset using natural language

Tool list

Use the exact tool names from the Tool list below when you call execute_tool. If you’re not sure which name to use, list the tools available for the current user first.

databoxmcp_ask_genie#Ask Genie, the Databox AI data analyst, to explore and analyze a dataset using natural language. Genie can answer business questions, run SQL queries, surface trends, and provide summaries.3 params▾

Ask Genie, the Databox AI data analyst, to explore and analyze a dataset using natural language. Genie can answer business questions, run SQL queries, surface trends, and provide summaries.

NameTypeRequiredDescription

dataset_idstringrequiredNo description.

questionstringrequiredNo description.

thread_idstringoptionalNo description.

databoxmcp_create_data_source#Create a new data source container in Databox for organizing datasets. Optionally scopes the data source to a specific account; defaults to the account of the authenticated API key.2 params▾

Create a new data source container in Databox for organizing datasets. Optionally scopes the data source to a specific account; defaults to the account of the authenticated API key.

NameTypeRequiredDescription

namestringrequiredNo description.

account_idstringoptionalNo description.

databoxmcp_create_dataset#Create a structured dataset within a Databox data source, optionally defining a column schema and primary keys for tabular data storage.4 params▾

Create a structured dataset within a Databox data source, optionally defining a column schema and primary keys for tabular data storage.

NameTypeRequiredDescription

data_source_idstringrequiredNo description.

namestringrequiredNo description.

columnsstringoptionalNo description.

primary_keysstringoptionalNo description.

databoxmcp_delete_data_source#Permanently delete a data source and all its associated datasets from Databox. This operation cannot be undone.1 param▾

Permanently delete a data source and all its associated datasets from Databox. This operation cannot be undone.

NameTypeRequiredDescription

data_source_idstringrequiredNo description.

databoxmcp_delete_dataset#Permanently delete a dataset and all its data from Databox. This operation cannot be undone.1 param▾

Permanently delete a dataset and all its data from Databox. This operation cannot be undone.

NameTypeRequiredDescription

dataset_idstringrequiredNo description.

databoxmcp_get_current_datetime#Get the current date and time in ISO 8601 format for a given timezone. Useful for resolving relative date expressions such as "last month" or "yesterday" before passing absolute dates to other tools.1 param▾

Get the current date and time in ISO 8601 format for a given timezone. Useful for resolving relative date expressions such as "last month" or "yesterday" before passing absolute dates to other tools.

NameTypeRequiredDescription

timezonestringoptionalNo description.

databoxmcp_get_dataset_ingestions#Retrieve the full ingestion history for a dataset, including job IDs, statuses, record counts, timestamps, and any error messages.1 param▾

Retrieve the full ingestion history for a dataset, including job IDs, statuses, record counts, timestamps, and any error messages.

NameTypeRequiredDescription

dataset_idstringrequiredNo description.

databoxmcp_get_ingestion#Get detailed information for a specific ingestion event, including status, timestamps, dataset metrics, and per-record ingestion outcomes.2 params▾

Get detailed information for a specific ingestion event, including status, timestamps, dataset metrics, and per-record ingestion outcomes.

NameTypeRequiredDescription

dataset_idstringrequiredNo description.

ingestion_idstringrequiredNo description.

databoxmcp_ingest_data#Push data records into an existing Databox dataset. Each record must match the dataset schema; data is validated against column types and constraints before ingestion.2 params▾

Push data records into an existing Databox dataset. Each record must match the dataset schema; data is validated against column types and constraints before ingestion.

NameTypeRequiredDescription

datastringrequiredNo description.

dataset_idstringrequiredNo description.

databoxmcp_list_accounts#List all Databox accounts accessible to the authenticated user. Use this to discover account IDs needed for other operations.0 params▾

List all Databox accounts accessible to the authenticated user. Use this to discover account IDs needed for other operations.

databoxmcp_list_data_source_datasets#List all datasets belonging to a specific Databox data source, including schema details, row counts, and metadata.1 param▾

List all datasets belonging to a specific Databox data source, including schema details, row counts, and metadata.

NameTypeRequiredDescription

data_source_idstringrequiredNo description.

databoxmcp_list_data_sources#List all API-ingestible data sources for a specific Databox account, returning IDs, names, types, and creation timestamps.1 param▾

List all API-ingestible data sources for a specific Databox account, returning IDs, names, types, and creation timestamps.

NameTypeRequiredDescription

account_idstringrequiredNo description.

databoxmcp_list_merged_datasets#List all merged datasets for a specific Databox account. Merged datasets combine data from multiple sources into a single unified dataset.1 param▾

List all merged datasets for a specific Databox account. Merged datasets combine data from multiple sources into a single unified dataset.

NameTypeRequiredDescription

account_idstringrequiredNo description.

databoxmcp_list_metrics#List all metrics available for a Databox data source, including metric keys, names, descriptions, and available dimensions. Pass the full metric_key value unchanged to load_metric_data.1 param▾

List all metrics available for a Databox data source, including metric keys, names, descriptions, and available dimensions. Pass the full metric_key value unchanged to load_metric_data.

NameTypeRequiredDescription

data_source_idintegerrequiredNo description.

databoxmcp_load_metric_data#Retrieve data points for a Databox metric over a date range with optional time-series granulation and dimension breakdown. The metric_key must be the exact value returned by list_metrics.8 params▾

Retrieve data points for a Databox metric over a date range with optional time-series granulation and dimension breakdown. The metric_key must be the exact value returned by list_metrics.

NameTypeRequiredDescription

data_source_idintegerrequiredNo description.

end_datestringrequiredNo description.

metric_keystringrequiredNo description.

start_datestringrequiredNo description.

dimensionstringoptionalNo description.

granulation_time_unitstringoptionalNo description.

is_whole_rangebooleanoptionalNo description.

record_limitstringoptionalNo description.