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FutureSearch MCP Server

Forecasts decisions and future outcomes, researches datasets, and returns probabilities, dates, numbers, or classifications.

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Install FutureSearch 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": {
    "futuresearch-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 5d ago.

Overview

FutureSearch MCP Server MCP server connects assistants to FutureSearch for forecasting decisions, future events, and dataset rows. It supports binary, numeric, date, categorical, thresholded, and conditional forecasts, with rationales returned alongside results. Research operations can investigate each row before forecasting or answer questions that are not about the future. Reach for it when an agent needs structured predictions grounded in a supplied question, options, context, or dataset.

Use cases

  • Forecast the probability of future events
  • Compare outcomes across decision options
  • Estimate dates and numeric ranges
  • Research each row of a dataset
  • Classify questions among multiple outcomes

Key features

  • Binary, numeric, date, categorical, and thresholded forecasts
  • Conditional forecasts with both premise outcomes
  • Decision forecasts across alternatives
  • Per-row web research with agent_map()
  • Rationales returned with forecast results

Security & health

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

ActiveLast checked Today

Recent health check succeeded.

9/9 checks healthy over the last 42d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 15h 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 commit11d 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 FutureSearch MCP Server works

What the FutureSearch MCP Server MCP server does

FutureSearch MCP Server MCP server gives an assistant access to FutureSearch forecasting and research operations. It can estimate the likelihood of a yes/no outcome, produce percentile ranges for a numeric value, estimate dates, compare categorical outcomes, or assign probabilities to threshold conditions. Forecasts include a rationale explaining the result.

Decision forecasting evaluates one outcome across multiple alternatives. Alternatives might represent a funding level, a policy choice, or another actor's possible action. The request can include context about the decision-maker, relevant facts, timing, resources, and previous actions. This information is useful when the answer depends on details that are not available on the web.

How it works

Forecast inputs are organized as rows in a dataset. A row contains a question and, where needed, columns for alternatives, categories, thresholds, or conditions. The service returns structured output for each row rather than only a prose answer.

Supported forecast forms include:

  • Binary probabilities from 0 to 100 for yes/no questions.
  • Numeric percentiles, from p10 through p90, for continuous quantities.
  • Date percentiles formatted as dates for timing questions.
  • Joint probabilities for a list of categorical outcomes.
  • Probabilities for ordered threshold conditions.
  • Conditional results showing outcomes both when a premise is true and when it is false.

Decision forecasts use a separate operation from general forecasts. For a decision, the service forecasts the selected outcome under each provided option.

Setup and configuration

The hosted MCP endpoint is https://mcp.futuresearch.ai/mcp. The README identifies connection instructions for Claude.ai, Claude Code, Gemini CLI, Codex CLI, and Cursor. The underlying Python SDK requires Python 3.12 or newer and installs from PyPI with pip install futuresearch, although an assistant using the hosted MCP endpoint does not need to install that package locally.

An API key is required for FutureSearch and can be obtained from the FutureSearch application. Forecast effort can be set to LOW or HIGH for supported operations and defaults to high; decision forecasts run at high effort. Current costs depend on the service's pricing.

Tools and capabilities

The documented capabilities cover:

  • Forecasting binary, numeric, date, categorical, thresholded, and conditional outcomes.
  • Comparing the outcomes of several decision alternatives.
  • Supplying shared context for questions about a user's own decision.
  • Adding resolution criteria for questions with an external source of truth.
  • Researching every row in a dataset with agent_map().
  • Returning rationales with forecast results.

Limitations and notes

Numeric forecasts require an output field and units. Date forecasts require an output field. Categorical forecasts require a field containing the possible outcomes, and thresholded forecasts require a field containing ordered threshold conditions. Conditional forecasts return separate values for the condition and its negation.

The deprecated everyrow package should not be used for new work; the README recommends migrating to futuresearch. Pricing varies by effort and operation, so confirm current costs before running large datasets.

Read the full README View source on GitHub

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Frequently Asked Questions about FutureSearch MCP Server

FutureSearch MCP Server is an MCP server that connects AI assistants to FutureSearch's forecasting and research service. Its main tools forecast decisions and future events, research every row of a dataset, and return probabilities, dates, numbers, categorical outcomes, threshold results, and rationales.

Use the hosted endpoint at https://mcp.futuresearch.ai/mcp rather than installing a local MCP package. The underlying Python SDK can be installed with pip install futuresearch and requires Python 3.12 or newer. An API key is required.

Yes, Cursor is named in the README's connection instructions. Configure Cursor to connect to the hosted MCP endpoint at https://mcp.futuresearch.ai/mcp and provide the required FutureSearch authentication.

It supports binary, numeric, date, categorical, thresholded, and conditional forecasts, as well as decision forecasts. Numeric and date requests require output fields, while categorical and thresholded requests require fields containing their outcome options or conditions.

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