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Documentation

Guides, use cases & API reference

  • Overview
    • Getting Started
    • Platform Overview
  • Features
    • Features Overview
    • AI Assistant
    • Workflow Automation
    • Intelligent Memory
    • Data Management
    • Universal Integrations
    • Communication Channels
    • Collective Agent Memory (CAML)
    • Security & Control
  • Use Cases Overview
  • Financial Services
  • Fraud Detection
  • Supply Chain
  • Technical Support
  • Software Development
  • Smart ETL
  • Data Governance
  • ESG Reporting
  • TAC Management
  • Reference
    • API Reference
  • Guides
    • Getting Started
    • Authentication
  • Endpoints
    • Workflows API
    • Tools API
    • KDL (Data Lake) API
    • OpenAI-Compatible API
    • A2A Protocol
    • Skills API
    • Knowledge Base (RAG) API
    • Communication Channels
    • DSL Import API
Release Notes

How to Connect a Data Source

Kaman supports 85+ integrations via the MCP (Model Context Protocol). This guide shows you how to connect an external data source — like a database, CRM, or cloud storage — so your agents can query it and sync data to your Lake House.

Overview

The connection flow has 4 steps:

  1. Create credentials — Store your authentication details via the Integrations page
  2. Create a data source — Select the MCP plugin and your credentials
  3. Select resources — Choose which tables, collections, or data to expose
  4. Sync to Lake House — Pull data into Kaman's built-in data lake (optional)

Step 1: Create Credentials

Before connecting, you need to store your access credentials.

  1. Go to Settings > Credentials
  2. Click + Add Credential
  3. Change Resource Type to MCP
  4. Search for your integration (e.g., "PostgreSQL", "Salesforce", "Google Sheets")
  5. Fill in the required fields
  6. Click Save

See Managing Credentials for detailed instructions.

Step 2: Create a Data Source

  1. Go to Build > My Resources in the sidebar, then click the Data Sources tab

  2. Or use the Sync Data to Lake House action from the Integrations page

  3. Select the MCP Plugin you want to connect (e.g., PostgreSQL)

  4. Choose the Credential Profile you created in Step 1

  5. Select the resources (tables, collections) to sync

  6. Click Create

Step 3: Select Resources

After creating the data source, you'll see the available resources (tables, collections, etc.):

  • Each resource appears as a tag on the data source card
  • Resources are automatically discovered from the connected service

Step 4: Sync to Lake House (Optional)

To sync data into Kaman's built-in data lake:

  1. Go to your connector card
  2. Click on a resource
  3. Configure column mappings (map source columns to target columns)
  4. Set the sync mode:
    • Full Sync — Replaces all data each time
    • Incremental Sync — Only syncs new/changed rows
  5. Click Sync
  6. Monitor sync progress

After sync, your data is available in Data > Lake House for querying.

Using Connected Data in Agents

Once connected, your agents can interact with the data source through tools:

Attach Tools to Agent

  1. Open your agent in the Agent Builder
  2. Go to Tools & Workflows tab
  3. Add the tools from your connected MCP (e.g., execute_query, list_tables)
  4. Save

Chat with Your Agent

Ask natural language questions and your agent will use the tools automatically:

  • "Show me all customers from the database"
  • "How many orders were placed last month?"
  • "Insert a new record into the products table"

Example: Connecting PostgreSQL

StepActionDetails
1Create credentialHost: your-db.example.com, Port: 5432, Database: mydb, Username: user, Password: ***
2Create data sourceSelect PostgreSQL MCP, choose credential profile
3ResourcesTables automatically listed: users, orders, products
4SyncOptional — sync orders table to Lake House
5Use in agentAttach execute_query, list_tables tools to your agent

Example: Connecting Google Sheets (OAuth)

StepActionDetails
1Create credentialSelect Google Sheets MCP, click Authorize, sign in with Google
2Create data sourceSelect Google Sheets MCP, choose credential
3ResourcesYour spreadsheets appear as resources
4SyncSync a sheet to Lake House as a table
5Use in agentAttach get_spreadsheet, update_values tools to agent

Supported Integrations

Browse all 85+ integrations at Discover > Integrations:

  • Databases — PostgreSQL, MySQL, MongoDB, Oracle, SQL Server, DynamoDB, Elasticsearch, and more
  • CRM — Salesforce, HubSpot, Zoho CRM, Pipedrive
  • E-Commerce — Shopify, Stripe, WooCommerce, Razorpay
  • Communication — Gmail, Outlook, Discord, Telegram, Slack, WhatsApp
  • Cloud Storage — Google Drive, Google Sheets, OneDrive, Dropbox, Airtable
  • Project Management — Jira, Trello, Asana, Monday, ClickUp
  • ERP — Odoo, ERPNext, NetSuite, SAP

Troubleshooting

IssueSolution
Connection test failsVerify credentials are correct. Check network access (firewall, IP allowlisting).
Resources not showingClick Refresh on the connector card. Check if the credential has read permissions.
Sync timeoutLarge tables may need increased timeout. Try syncing a smaller table first.
Schema changes not detectedGo to data source > Check Schema. Update column mappings if changed.
OAuth token expiredEdit the credential and re-authorize. Tokens auto-refresh in most cases.

On this page

  • Overview
  • Step 1: Create Credentials
  • Step 2: Create a Data Source
  • Step 3: Select Resources
  • Step 4: Sync to Lake House (Optional)
  • Using Connected Data in Agents
  • Attach Tools to Agent
  • Chat with Your Agent
  • Example: Connecting PostgreSQL
  • Example: Connecting Google Sheets (OAuth)
  • Supported Integrations
  • Troubleshooting