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Everyrow MCP Server

Connects MCP clients to FutureSearch for forecasts, decisions, and web research over tabular inputs.

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Install Everyrow MCP Server

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "everyrow-mcp-server": {
      "command": "uvx",
      "args": [
        "futuresearch"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

uvx futuresearch

Process exited before answering initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 8d ago.

Overview

Everyrow MCP Server MCP server connects compatible assistants to FutureSearch's hosted MCP endpoint for forecasting and research operations. It can evaluate decisions, forecast binary, numeric, date, categorical, thresholded, and conditional outcomes, and research each row in a dataset. Forecast results include rationale fields, with percentile or probability outputs depending on the requested type. Reach for it when an agent needs structured predictions or web research over rows rather than a general conversational answer.

Use cases

  • Forecast outcomes for competing decisions
  • Estimate dates and numeric future values
  • Compare probabilities across categorical outcomes
  • Research every row in a dataset
  • Add context and resolution criteria to forecasts

Key features

  • Decision forecasts across alternatives
  • Binary, numeric, date, categorical, and thresholded forecasts
  • Conditional forecasts with rationale output
  • Row-by-row web research
  • Hosted MCP endpoint
  • Python SDK support

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

10/10 checks healthy over the last 45d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 4d ago via OSV.dev · futuresearch (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars57Stargazers on the source repository.
Last commit14d agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How Everyrow MCP Server works

What Everyrow MCP Server MCP server does

Everyrow MCP Server MCP server exposes FutureSearch capabilities to assistants through the Model Context Protocol. The underlying service turns questions about future events and decisions into structured results such as probabilities, dates, numbers, or categorical outcome probabilities. It also supports research operations for building forecast inputs or answering questions that are not about the future.

A decision forecast evaluates an outcome under each option in a decision. For example, an agent can compare several grant amounts and estimate the resulting number of parliamentarians. The request can include context about the decision-maker, relevant facts, timing, money, and prior actions that are not available on the web.

How it works

The hosted MCP endpoint is https://mcp.futuresearch.ai/mcp. An assistant sends forecast or research requests through that endpoint and receives structured output. Forecast operations work with tabular inputs, where each row contains a question and, when needed, fields such as alternatives, categories, thresholds, or conditions.

Supported forecast forms include binary questions, numeric estimates, dates, multiple-choice outcomes, threshold probabilities, and forecasts conditioned on a premise. Numeric and date forecasts return percentile estimates. Categorical forecasts return probabilities across the supplied options, while conditional forecasts provide results both given and not given the condition. Forecast results include a rationale column explaining the answer.

Setup and configuration

Connect an MCP-compatible client to https://mcp.futuresearch.ai/mcp. The README specifically documents connections for Claude.ai, Claude Code, Gemini CLI, Codex CLI, and Cursor. The Python SDK is installed separately with pip install futuresearch and requires Python 3.12 or newer. Its API key is obtained from the FutureSearch application, and the SDK documentation notes that the older everyrow package is deprecated in favor of futuresearch.

Consult the client-specific connection instructions and FutureSearch documentation before deploying the connection.

Tools and capabilities

  • Forecast a binary yes/no question with a probability.
  • Estimate numeric quantities using percentile outputs and units.
  • Estimate dates using percentile dates in YYYY-MM-DD format.
  • Compare categorical outcomes with jointly calculated probabilities.
  • Evaluate threshold conditions or forecasts under a stated condition.
  • Forecast decisions across alternatives, including decisions made by another party.
  • Run agent_map() web research across every row of a dataset.
  • Use multi_agent() research to build forecast inputs or answer non-forecast questions.

Forecasts can use low or high effort where the operation exposes that choice. Decision forecasts run at high effort, and categorical, thresholded, and conditional forecasts require high effort. Costs vary by operation and effort level; the README directs users to the current pricing information.

Limitations and notes

Forecast quality and interpretation depend on clearly stated questions, options, context, and resolution criteria. Numeric and date requests require an output field, and numeric requests also require units. Categorical requests need a field containing the possible outcomes; the README recommends making that set exhaustive and adding an “Other” option when appropriate.

The material identifies the MCP service as hosted, but does not provide a local MCP server installation procedure. The Python SDK and MCP connection are related access paths, but the documented SDK commands should not be assumed to configure the hosted endpoint automatically.

Read the full README View source on GitHub

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Frequently Asked Questions about Everyrow MCP Server

Everyrow MCP Server is an MCP server that connects assistants to FutureSearch's hosted forecasting and research service. Its main tools support decision forecasts, binary and structured forecasts, row-by-row web research with `agent_map()`, and multi-agent research with `multi_agent()`. Results can include probabilities, percentiles, dates, and rationales.

For Python use, install the replacement SDK with `pip install futuresearch` on Python 3.12 or newer.

Yes, Cursor is one of the clients named in the README. Configure it to connect to the hosted MCP endpoint at `https://mcp.futuresearch.ai/mcp` using the client connection instructions.

It supports binary, numeric, date, categorical, thresholded, conditional, and decision forecasts. Depending on the type, results contain probabilities, percentile estimates, dates, outcome probabilities, or rationales.

The `everyrow` package still works but is deprecated. New Python integrations should migrate to the `futuresearch` package, which requires Python 3.12 or newer.

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