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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 is a data platform that connects to 160+ APIs, web scrapers, and third-party services so you can collect, enrich, and act on structured data without building pipelines. Use it however fits your workflow: through the visual table interface, the REST API, the Python SDK, the CLI, or directly inside AI agents like Claude via the MCP server. At its core, Databar gives you keyless access to a network of data providers. Point at a data source, run it, and get results back in seconds. Tables are the primary way most users interact with Databar, but the same enrichment and waterfall engine is available programmatically for developers who want to integrate it into their own applications and automations.
Databar platform overview showing a table with enriched data

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Core capabilities

Tables

Store and manage structured data with columns, filters, and cell-level enrichment statuses.

Enrichments

Combine your existing data with third-party providers to fill in the gaps automatically.

API Network

Access 100+ keyless data providers through the API Network. Pay per use with credits.

Developer tools

REST API, Python SDK, CLI, and MCP server for programmatic access.

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How it works

Databar combines several building blocks into a single workflow:
  • Enrichments: attach data providers to your table columns. When you run an enrichment, each row is sent to the provider and the results are written back into your table.
  • Waterfalls: chain multiple providers together with automatic fallback. If the first provider returns no data, the next one picks up.
  • Connectors: bring your own API keys for services you already pay for, or add entirely custom endpoints.
  • Exporters: push enriched data to HubSpot, Salesforce, Google Sheets, webhooks, or other tables.
  • Formulas: transform data in-place with Excel formulas, JQ expressions, merge columns, deduplication, and Table Lookup (VLOOKUP across tables).

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AI features

  • AI Researcher: an autonomous agent that finds and compiles information across the web for each row in your table.
  • AI Prompt Templates: run custom prompts against your data using LLMs, with configurable templates and variables.

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Extensions and integrations

  • Chrome Extension: collect structured data from any website directly into a Databar table.
  • Google Sheets Extension: run Databar enrichments without leaving your spreadsheet.
  • n8n Integration: connect Databar to n8n workflows for complex automation scenarios.
  • Webhooks: receive data from external systems instantly, with zero-config webhook URLs per table.

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Who is Databar for?

Databar is built for teams that need structured data but don’t want to maintain pipelines:
  • Sales and RevOps: enrich leads, verify contact info, score accounts
  • Marketing: build prospect lists, research competitors, monitor mentions
  • Recruiting and HR: source candidates, verify profiles, track outreach
  • E-commerce: monitor pricing, aggregate product data, track suppliers
  • Anyone with a data workflow: the platform is flexible enough to handle any scenario where you need to collect, enrich, or transform structured data

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Recent additions

Databar ships updates frequently. Check out our changelog to see the latest features and improvements.

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Get started

Tables

Create your first table

REST API

Quickstart for the REST API

Python SDK

Install the Python SDK

MCP Server

Connect via MCP

Enrichments

Learn about enrichments

Credits

Understand pricing