Databar.ai

One subscription, 160+ data sources.

MCPAPINative readiness

About

What is Databar.ai

Databar.ai connects to over 160 APIs, web scrapers, and third-party data providers to let GTM teams collect, enrich, and automate data workflows from a single interface. It's built for GTM Engineers, RevOps practitioners, and GTM Leads at mid-market and enterprise companies who need to move fast on outbound without standing up custom data infrastructure. The standout capability is Waterfall enrichment — when one provider returns no result, Databar automatically cascades through the next until it finds a match, which meaningfully improves coverage on hard-to-find contacts. Teams can access everything via a visual table interface, REST API, SDK, or directly through an MCP server for AI agent workflows. Where it starts to strain is at the lower end: the $99 Build plan caps batch enrichments at 10k rows and limits simultaneous requests to five, so high-volume teams will need the $495 Scale tier or above to avoid bottlenecks.

Data & Discovery

Capabilities

Key features

Data enrichment

fills records with firmographic, technographic, or signal data

AI capabilities

autonomous multi-step actions, per-prospect AI research

Data

intent signals, real-time enrichment, firmographic + tech-stack data

Also ships

Chrome extension, official SDK

Outbound webhooks

event-driven integrations

Our verdict

Vanderbuild take

We see Databar as the data infrastructure layer that GTM Engineers and RevOps teams reach for when they've outgrown single-provider enrichment tools and need coverage, flexibility, and programmatic access in one place. The agentic readiness here is native — Databar ships an MCP server, meaning AI agents can call enrichment and scraping workflows directly without any middleware, which makes it a credible orchestration layer for teams building agentic outbound stacks. Budget for an experienced operator — the credit-based pricing at $495/month for Scale is reasonable for the volume, but vanilla setup leaves real value on the table given how many providers and workflow options exist. The honest limitation: the platform's depth is also its friction — teams without a dedicated GTM Engineer or RevOps owner will likely underutilize it, and the lower-tier plan's row and request caps will frustrate anyone running high-frequency enrichment at scale.

Mateusz Sekta

Founder, vanderbuild

The wedge

Agentic stack profile

MCP server

Yes

Live MCP server — agents can call this tool directly.

Connects Databar to AI tools like Claude and Cursor, enabling AI agents to discover and execute data enrichments using natural language.

Open MCP →

API

REST

Programmatic access available.

REST API — straightforward to call from any agent or workflow tool. Rate limits and auth vary by plan.

API docs →

Agentic readiness

Native

Built for agents from the ground up.

MCP server + agent-friendly API + at least one autonomous workflow out of the box. The bar for 'Native' is high — only a handful of tools currently qualify.

Stack role

Enricher · Data source · Signal source

Where this tool slots into an agentic pipeline.

Plays the role of Enricher + Data source + Signal source in an agentic pipeline. Use it to add firmographic, technographic, and signal data to a lead row on the fly; source contacts, companies, and the raw inputs an agent needs to act.

Compare

Databar.ai alternatives

Tools that solve a similar problem — compared at a glance.

Databar.ai(this tool)

Pricing: Usage-based
Budget: $$$
Best for: GTM Engineer, RevOps

Readiness: Native
MCP: Yes
API: REST

Clay

Pricing: Freemium
Budget: $$$
Best for: RevOps, GTM Engineer

Readiness: Native
MCP: Yes
API: REST

Captain Data

Pricing: Usage-based
Budget: $$$
Best for: GTM Engineer, RevOps

Readiness: Native
MCP: Yes
API: REST

Answers

Frequently asked questions

Does Databar.ai have an MCP server?

Yes — Databar.ai exposes a Model Context Protocol server. Connects Databar to AI tools like Claude and Cursor, enabling AI agents to discover and execute data enrichments using natural language. See the MCP docs at https://docs.databar.ai/mcp-server.

Does Databar.ai have a public API?

Yes — Databar.ai ships a REST API. Docs: https://docs.databar.ai/api-reference/introduction.

How much does Databar.ai cost?

Databar.ai: pricing is usage-based, expect higher tier ($$$) spend. Full pricing page: https://databar.ai/pricing.

Who is Databar.ai best for?

Databar.ai is built for GTM Engineer, RevOps, GTM Lead. Fits Mid-market (50-500), Enterprise-sized teams.

How well does Databar.ai fit an agentic sales stack?

Tier: Native. Has both an MCP server and an agent-friendly API — drops into an agentic stack with minimal glue code.

Quick spec

MCP serverYes ↗

APIREST ↗

ReadinessNative

Stack roleEnricher, Data source, Signal source

Pricing

Usage-based

$$$

Vendor pricing →

Ideal customer

Growth stage

Scale-up · Enterprise

Company size

Mid-market (50-500) · Enterprise

Best for

GTM Engineer · RevOps · GTM Lead