Deduplicate Contact Lists

Add the everyrow connector if you haven't already. Then upload both contact CSVs and ask Claude:

Merge these two contact lists to find the same person across both. Account for nicknames (Bob/Robert, Mike/Michael, Tom/Thomas), initials (S. Chen = Sarah Chen), and institution matching. When in doubt, favor false negatives over false positives.

7 matches found, 5 correctly left unmatched. Results are nearly instant.

Claude Code can diff two lists. But what if "Dr. Sarah Chen" on one list is "S. Chen" on another, and "Robert Johnson" appears as "Bob Johnson"? You need semantic matching that understands nicknames and initials.

Here, we get Claude Code to merge two overlapping contact lists (12 and 10 people) to identify duplicates across name formats.

Add everyrow to Claude Code if you haven't already:

claude mcp add futuresearch --scope project --transport http https://mcp.futuresearch.ai/mcp

With both contact CSVs in your working directory, tell Claude:

Merge these two contact lists to find the same person across both. Account for
nicknames (Bob/Robert, Mike/Michael, Tom/Thomas), initials (S. Chen = Sarah Chen),
and institution matching. When in doubt, favor false negatives over false positives.

Claude calls everyrow's merge MCP tool:

Tool: everyrow_merge
├─ task: "Match contacts between two lists to identify the same person..."
├─ left_csv: "/Users/you/contacts_list_a.csv"
├─ right_csv: "/Users/you/contacts_list_b.csv"
├─ merge_on_left: "name"
├─ merge_on_right: "full_name"
└─ relationship_type: "one_to_one"

→ Submitted: 12 rows for merging.
  Session: https://futuresearch.ai/sessions/1d39b32d-d71e-48e8-8ac7-de907f86745a

Tool: everyrow_results
→ Saved 12 rows to /Users/you/merged_contacts.csv

7 matches found, 5 correctly left unmatched. View the session.

David Kim, Anna Kowalski, Maria Santos, Jennifer Park, and Christopher Davis were correctly left unmatched (no counterpart in the other list). The merge handled all variations via fuzzy matching without needing LLM calls.

The everyrow SDK's merge() identifies the same person across lists with different name formats, nicknames, and initials.

pip install everyrow
export EVERYROW_API_KEY=your_key_here  # Get one at futuresearch.ai/api-key

import asyncio
import pandas as pd
from everyrow import create_session
from everyrow.ops import merge

list_a = pd.read_csv("contacts_list_a.csv")
list_b = pd.read_csv("contacts_list_b.csv")

async def main():
    async with create_session(name="Contact List Merge") as session:
        result = await merge(
            session=session,
            task="""
                Match contacts between two lists to identify the same person.
                Account for nicknames (Bob/Robert, Mike/Michael, Tom/Thomas),
                initials (S. Chen = Sarah Chen), and institution matching.
                Favor false negatives over false positives.
            """,
            left_table=list_a,
            right_table=list_b,
            merge_on_left="name",
            merge_on_right="full_name",
        )
        return result.data

merged = asyncio.run(main())

7 matches found at $0.00. The merge correctly handled nickname matching (Bob/Robert, Mike/Michael, Tom/Thomas), initial matching (S. Chen to Sarah Chen), and left 5 contacts unmatched when no counterpart existed.