everyrow-mcp 0.2.1

MCP server for everyrow: agent ops at spreadsheet scale

everyrow MCP Server

MCP (Model Context Protocol) server for everyrow: agent ops at spreadsheet scale.

This server exposes everyrow's 5 core operations as MCP tools, allowing LLM applications to screen, rank, dedupe, merge, and run agents on CSV files.

All tools operate on local CSV files. Provide absolute file paths as input, and transformed results are written to new CSV files at your specified output path.

Installation

The server requires an everyrow API key. Get one at everyrow.io/api-key ($20 free credit).

Claude Desktop

Download the latest .mcpb bundle from the GitHub Releases page and double-click to install in Claude Desktop. You'll be prompted to enter your everyrow API key during setup.

Note: The MCPB bundle works in Claude Desktop's Chat mode. Due to a known limitation, local MCP servers are not currently exposed in Cowork mode.

Cursor

Set the environment variable in your terminal shell before opening cursor. You may need to re-open cursor from your shell after this. Alternatively, hardcode the api key within cursor settings instead of the hard-coded ${env:EVERYROW_API_KEY}

export EVERYROW_API_KEY=your_key_here

Manual Config

Either set the API key in your shell environment as mentioned above, or hardcode it directly in the config below. Environment variable interpolation may differ between MCP clients.

export EVERYROW_API_KEY=your_key_here

Add this to your MCP config. If you have uv installed:

{
  "mcpServers": {
    "everyrow": {
      "command": "uvx",
      "args": ["everyrow-mcp"],
      "env": {
        "EVERYROW_API_KEY": "${EVERYROW_API_KEY}"
      }
    }
  }
}

Alternatively, install with pip (ideally in a venv) and use "command": "everyrow-mcp" instead of uvx.

Available Tools

everyrow_screen

Filter CSV rows based on criteria that require judgment.

Parameters:
- task: Natural language description of screening criteria
- input_csv: Absolute path to input CSV
- output_path: Directory or full .csv path for output

Example: Filter job postings for "remote-friendly AND senior-level AND salary disclosed"

everyrow_rank

Score and sort CSV rows based on qualitative criteria.

Parameters:
- task: Natural language description of ranking criteria
- input_csv: Absolute path to input CSV
- output_path: Directory or full .csv path for output
- field_name: Name of the score field to add
- field_type: Type of field (float, int, str, bool)
- ascending_order: Sort direction (default: true)

Example: Rank leads by "likelihood to need data integration solutions"

everyrow_dedupe

Remove duplicate rows using semantic equivalence.

Parameters:
- equivalence_relation: Natural language description of what makes rows duplicates
- input_csv: Absolute path to input CSV
- output_path: Directory or full .csv path for output

Example: Dedupe contacts where "same person even with name abbreviations or career changes"

everyrow_merge

Join two CSV files using intelligent entity matching.

Parameters:
- task: Natural language description of how to match rows
- left_csv: Absolute path to primary CSV
- right_csv: Absolute path to secondary CSV
- output_path: Directory or full .csv path for output
- merge_on_left: (optional) Column name in left table
- merge_on_right: (optional) Column name in right table

Example: Match software products to parent companies (Photoshop -> Adobe)

everyrow_agent

Run web research agents on each row of a CSV.

Parameters:
- task: Natural language description of research task
- input_csv: Absolute path to input CSV
- output_path: Directory or full .csv path for output

Example: "Find this company's latest funding round and lead investors"

Output Path Handling

The output_path parameter accepts two formats:

1. Directory: Output file is named {operation}_{input_name}.csv

   • Input: /data/companies.csv, Output path: /output/
   • Result: /output/screened_companies.csv

2. Full file path: Use the exact path specified

   • Output path: /output/my_results.csv
   • Result: /output/my_results.csv

The server validates output paths before making API requests to avoid wasted costs.

Development

cd everyrow-mcp
uv sync
uv run pytest

For MCP registry publishing:

mcp-name: io.github.futuresearch/everyrow-mcp

License

MIT - See LICENSE.txt