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Everyrow MCP Server

@futuresearch

Repository

Basic information

Category

Other

Transports

stdio

Author

futuresearch

Submitted by

Callum McMahon

About Everyrow MCP Server

No overview available yet

Everyrow MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

Everyrow MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

What is Everyrow?

Everyrow is an SDK to run LLM research agents at scale on pandas dataframes, enabling intelligent sorting, filtering, merging, deduplication, and column addition. It scales to tens of thousands of LLM calls or web research agents from a single Python method.

How to use Everyrow?

Install via pip install everyrow, get an API key at everyrow.io/api-key, then use Python with pandas and Pydantic models to run operations like screen, rank, or research. Integrate with Claude Code, Gemini CLI, Codex CLI, or Cursor via plugins.

Key features of Everyrow

  • Screen rows by criteria that need judgment (10k rows)
  • Rank and score rows from research (10k rows)
  • Deduplicate where fuzzy matching fails (20k rows)
  • Merge tables when keys don't match (5k rows)
  • Web research agents on every row (10k rows)
  • Async operations with crash recovery via fetch_task_data

Use cases of Everyrow

  • Filter job listings for remote-friendly senior roles with salary disclosure
  • Deduplicate messy CRM exports where exact matching fails
  • Merge two tables without clean join keys
  • Research company funding rounds and lead investors at scale
  • Classify and label thousands of dataframe rows using LLM judgment

Frequently asked questions

1

What Python version is required?

Python 3.12 or newer is required to install and run Everyrow.

2

How do I get an API key?

Go to everyrow.io/api-key to obtain an API key; you receive $20 in free credit.

3

Can Everyrow perform web research?

Yes, use `single_agent` for one row or `agent_map` for parallel web research agents on every row, tuned for deep research and cost efficiency.

4

What if my script crashes during an async operation?

Print the task ID after submitting, then use `fetch_task_data` with that ID to recover the results later.

5

Which coding agent plugins are supported?

Plugins are available for Claude Code, Gemini CLI, Codex CLI, and Cursor, each with installation steps in the README.

Comments

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