Databar MCP Server

A Model Context Protocol (MCP) server that enables AI assistants like Claude to interact with Databar.ai's data enrichment API. Discover, configure, and run data enrichments across hundreds of data providers using natural language.

Features

Smart Enrichment Discovery — Search and filter enrichments by keyword or category
Natural Language Interface — Ask "get David's LinkedIn profile" and the right enrichment runs automatically
Bulk Operations — Enrich many records in a single call with bulk enrichment and bulk waterfall support
Table Management — Create tables, manage columns, insert/update/upsert rows
Waterfall Support — Try multiple data providers sequentially until one succeeds
Async Handling — Automatic polling for results with no manual intervention
Intelligent Caching — 24-hour result cache reduces API calls and costs
Error Handling — Retries with exponential backoff and clear error messages

Quick Start

Prerequisites

Node.js 18+
A Databar.ai API key (get one here)

Install & Build

bash

git clone https://github.com/databar-ai/databar-mcp-server.git
cd databar-mcp-server
npm install
npm run build

Configure Claude Desktop

Edit your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

config.json

{
  "mcpServers": {
    "databar": {
      "command": "node",
      "args": ["/absolute/path/to/databar-mcp-server/dist/index.js"],
      "env": {
        "DATABAR_API_KEY": "your-api-key-here"
      }
    }
  }
}

Restart Claude Desktop. Verify by asking: "What Databar tools do you have access to?"

Usage Examples

Find someone's LinkedIn profile

"Get me David Abaev's LinkedIn profile"

Claude searches for LinkedIn enrichments, picks the right one, runs it, and returns the profile data.

Verify an email address

"Verify the email david@databar.ai"

Find an email using waterfall

"Find the email for John Smith at Google"

Runs a waterfall that tries multiple providers until one returns a result.

Bulk enrich a list

"Enrich these 10 emails with company data: [list]"

Uses bulk enrichment to process all records in a single API call.

Manage table data

"List my tables"
"Create 5 rows in table abc-123 with columns name and email"
"Get the columns for table abc-123"

Available Tools

Enrichments

Tool	Description
`search_enrichments`	Search enrichments by keyword or category
`get_enrichment_details`	Get parameters, pricing, and response fields for an enrichment
`run_enrichment`	Run a single enrichment (with auto-polling and caching)
`run_bulk_enrichment`	Run an enrichment on multiple inputs at once

Waterfalls

Tool	Description
`search_waterfalls`	Search available waterfall enrichments
`run_waterfall`	Run a waterfall (tries providers sequentially)
`run_bulk_waterfall`	Run a waterfall on multiple inputs at once

Tables

Tool	Description
`create_table`	Create a new empty table
`list_tables`	List all tables in your workspace
`get_table_columns`	Get column schema for a table
`get_table_rows`	Get rows with pagination
`get_table_enrichments`	List enrichments configured on a table
`add_table_enrichment`	Add an enrichment to a table with column mapping
`run_table_enrichment`	Trigger an enrichment on all rows in a table

Row Operations

Tool	Description
`create_rows`	Insert up to 50 rows with deduplication options
`patch_rows`	Update fields on existing rows by ID
`upsert_rows`	Insert or update rows based on a matching key

Account

Tool	Description
`get_user_balance`	Get credit balance and account info

Configuration

All settings are configurable via environment variables:

Variable	Default	Description
`DATABAR_API_KEY`	(required)	Your Databar API key
`DATABAR_BASE_URL`	`https://api.databar.ai/v1`	API base URL
`CACHE_TTL_HOURS`	`24`	Result cache TTL in hours
`MAX_POLL_ATTEMPTS`	`150`	Max polling attempts for async tasks
`POLL_INTERVAL_MS`	`2000`	Polling interval in ms

How It Works

Async Task Handling

Server sends a run request to the Databar API
API returns a task_id
Server automatically polls /v1/tasks/{task_id} every 2 seconds
When status is completed, results are returned
If data has expired (1-hour retention), gone status is handled gracefully

Caching

Results are cached for 24 hours by default
Cache key: enrichment ID + serialized params
Cached results don't consume credits
Use skip_cache: true to force fresh data

Smart Categorization

Enrichments are automatically categorized (People, Company, Email, Phone, Social, Financial, Verification) to help the AI assistant pick the right tool.

Development

Terminal

npm run dev      # Run with tsx (hot reload)
npm run build    # Compile TypeScript
npm start        # Run compiled output

Project Structure

Code

databar-mcp-server/
├── src/
│   ├── index.ts           # MCP server entry point & tool handlers
│   ├── databar-client.ts  # Databar API client with polling
│   ├── cache.ts           # In-memory cache with TTL
│   ├── types.ts           # TypeScript type definitions
│   └── utils.ts           # Helpers & categorization
├── dist/                  # Compiled output (generated)
├── package.json
├── tsconfig.json
└── .gitignore

Troubleshooting

Problem	Solution
Server not connecting	Verify API key, rebuild (`npm run build`), restart Claude Desktop
"No enrichments found"	Try a broader search query; list cache refreshes every 5 minutes
"Task timed out"	Some enrichments take longer; increase `MAX_POLL_ATTEMPTS`
"Task data has expired"	Data is stored for 1 hour only; re-run the enrichment
"Invalid API key"	Check `.env` or Claude Desktop config for typos/extra spaces

Resources

License

MIT