databar-bulk-enrichment

Bulk-enrich CSV, JSON, or plain text records via Databar MCP endpoints.

3|2|Updated Feb 25, 2026
One-click install
npx skills add https://github.com/databar-ai/databar-mcp-server --skill databar-bulk-enrichment
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: databar-bulk-enrichment
Source: https://github.com/databar-ai/databar-mcp-server/tree/main/skills/databar-bulk-enrichment
Command: npx skills add https://github.com/databar-ai/databar-mcp-server --skill databar-bulk-enrichment

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bulk-enrich multiple records in a single operation, returning inline results without creating a persistent table.

Core Features & Use Cases

  • Parse input lists in CSV, JSON, or plain text and map each item to the required enrichment parameters.
  • Automatically select and run the appropriate bulk enrichment or waterfall flow based on dataset size and user intent.
  • Return concise results inline for smaller datasets and offer table-based workflows for larger datasets.

Quick Start

Provide a list of records (CSV, JSON, or plain text) and I will bulk-enrich them inline using Databar.

Frequently Asked Questions about databar-bulk-enrichment

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I bulk-enrich multiple records in a single operation?

To bulk-enrich multiple records, provide an input list in CSV, JSON, or plain text. The system automatically parses the input, maps items to required enrichment parameters, and returns concise inline results without creating a persistent table.

Do I need the Databar MCP server to run bulk data enrichment workflows?

Yes, the Databar MCP server is required for bulk data enrichment. It connects to bulk enrichment endpoints to validate required fields and process multiple records in a single operation.

What's the best way to enrich a large dataset without creating a persistent table?

For enriching a large dataset, use a table-based or waterfall workflow. The system automatically selects these flows based on dataset size, returning inline results for smaller datasets and switching workflows for larger ones.

What input formats are supported for parsing and mapping bulk enrichment parameters?

Supported input formats for parsing bulk enrichment parameters include CSV, JSON, and plain text. The system automatically maps each item from these formats to the required fields for the enrichment operation.

When should I use waterfall flows instead of inline bulk enrichment?

You should use waterfall flows instead of inline bulk enrichment for larger datasets. The system switches to waterfall or table-based workflows based on dataset size, while returning concise inline results for smaller conversational datasets.