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Documentation Index

Fetch the complete documentation index at: https://docs.databar.ai/llms.txt

Use this file to discover all available pages before exploring further.

Databar gives you programmatic access to 160+ integrations, enrichment workflows, waterfall logic, and structured tables. Use the REST API, Python SDK, CLI, or MCP server to build data pipelines, enrich your CRM, score leads, or let AI agents handle research for you.

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Get started

Pick the path that fits your stack. Each guide walks you through authentication and your first successful request.

REST API

Raw HTTP with cURL, JavaScript, or any language. Start here if you want full control.

Python SDK

Typed client with built-in polling and error handling. Install with pip install databar.

CLI

Run enrichments and manage tables from your terminal. Great for scripting and AI agents.

MCP Server

Connect Databar to Claude, Cursor, or other MCP-compatible AI tools. No code required.

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Core concepts

Enrichments are the building blocks. Each enrichment connects to a data provider (LinkedIn, Clearbit, Hunter, etc.) and returns structured data for a given input. You can run enrichments individually, in bulk, or attach them to a table. Browse enrichments Waterfalls chain multiple providers together for the same lookup. If the first provider returns no result, the next one is tried automatically. This maximizes coverage without writing fallback logic yourself. Browse waterfalls Tables are structured datasets that live in your Databar workspace. You create a table, insert rows, attach enrichments or waterfalls, and run them across all rows. Results are stored in the table and accessible via the API or the Databar UI. Tables API Connectors let you bring your own API credentials for supported providers, or define custom HTTP endpoints that Databar can call as enrichment sources. Connectors API Exporters push data from your tables into external destinations like HubSpot, Salesforce, Google Sheets, or custom webhooks. Browse exporters Tasks represent async operations. When you run an enrichment or waterfall, you get back a task_id. Poll the task endpoint to check status and retrieve results. Task data is stored for 24 hours. Tasks API

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What you can build

  • Lead enrichment pipelines that pull company data, emails, and phone numbers for every new signup or CRM import. Walkthrough
  • Waterfall email finders that try multiple providers until they find a verified email. Walkthrough
  • Table-driven enrichment workflows where you create a table, add rows, attach enrichments, and run everything in a few API calls. Walkthrough
  • AI-powered research agents that use the MCP server to discover and run enrichments with natural language. MCP quickstart

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Explore

API Reference

All endpoints with request and response examples.

MCP Server

Connect Databar to Claude, Cursor, and other AI tools.

Product Guide

Learn how Databar works from the UI perspective.