Everyrow MCP Server
Run per-row forecast, score, classify, or research jobs on tabular datasets from your coding agent via Everyrow’s API.
Overview
Everyrow MCP is an MCP server for the idea phase that lets agents forecast, score, classify, or research each row of a dataset through Everyrow’s API.
What is this MCP server?
- PyPI package everyrow-mcp (version 0.5.1) with stdio transport via uvx
- Per-row operations: forecast, score, classify, and deep research on datasets
- Requires EVERYROW_API_KEY from https://everyrow.io/api-key
- Positioned as giving your AI a research team for tabular work
- Lives in futuresearch/everyrow-sdk monorepo under everyrow-mcp subfolder
- Package version 0.5.1 on PyPI identifier everyrow-mcp
- stdio transport with uvx runtime hint
- One required secret: EVERYROW_API_KEY
What problem does it solve?
Solo builders stall on idea-stage tables because enriching every row with research or scores means brittle scripts instead of agent-native tools.
Who is it for?
Founders validating markets with lead lists, competitor grids, or survey exports who want agent-driven row enrichment.
Skip if: Teams that need only a single web search or a fully managed warehouse ETL without per-row AI operations.
What do I get? / Deliverables
After you set EVERYROW_API_KEY and register the stdio server, your agent can batch row-level research and scoring inside the same session as your code.
- Enriched datasets with per-row forecasts, scores, or classifications
- Agent-orchestrated research batches without one-off Python glue
- Documented stdio MCP entry in your agent config
Recommended MCP Servers
Journey fit
Early journey research—competitive lists, lead tables, and market datasets—happens in idea before you hard-commit to a product shape. Row-level enrichment and classification are classic research workflows when you are still exploring segments and signals.
How it compares
Row-batch research MCP backed by Everyrow, not a static RAG skill or local file grep tool.
Common Questions / FAQ
Who is Everyrow MCP for?
Solo and indie builders using Claude Code, Cursor, or similar agents who work from datasets and want automated per-row research without custom scrapers.
When should I use Everyrow MCP?
Use it during idea research when you need to classify, score, or research many rows before you finalize positioning or build a prototype.
How do I add Everyrow MCP to my agent?
Configure the PyPI package everyrow-mcp with stdio transport (uvx), set the EVERYROW_API_KEY environment variable, and point your MCP client at that server.