everyrow-mcp 0.1.9

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.

Setup

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

Either set the API key in your shell environment, or hardcode it directly in the config below.

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
- select_representative: Keep one row per duplicate group (default: true)

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