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391,827 tools. Last updated 2026-08-04 21:28

"Hypothesis" matching MCP tools:

  • Conduct named statistical hypothesis tests by specifying the test name, sample data, parameters, significance level, and alternative hypothesis.
  • List product experiments to review hypotheses, metrics, and outcomes. Filter by product or state to find concluded verdicts for evidence when proposing new work.
    MIT
  • Critique a draft feature spec, experiment plan, or page against baseline PM standards—clear problem, success metric, evidence, risks, and rollout—and receive structured findings with severity, concrete fixes, and a 0-100 score.
    MIT
  • Create a product experiment to track Build-Measure-Learn stages, including title, metric, target, and hypothesis. Manage experiments from idea to learning.
    MIT
  • Update a product experiment's Build-Measure-Learn stage, outcome, and next decision. Use experiment id from list_experiments; record validated/invalidated and pivot/persevere.
    MIT

Matching MCP Servers

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    Enables AI-powered academic research workflow from keyword search to hypothesis generation. Integrates multiple AI models to automatically search ArXiv papers, extract key information, and generate innovative research hypotheses for researchers.
    Last updated
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Matching MCP Connectors

  • Find novel, statistically validated patterns in tabular data — hypothesis-free.

  • Variable relationships from research papers with causal direction and source traceback.

  • Run statistical hypothesis tests (t-test, ANOVA, chi-square, correlation) and get p-value with reject/fail-to-reject decision at your chosen alpha level.
    MIT
  • Identifies root causes of service-level indicator anomalies by testing causal propagation from ancestor metrics and adjusting for indirect causes using statistical hypothesis testing.
    MIT
  • Update an active intent's title, description, scope, or constraints to reflect evolving understanding during work. Use when the real problem, scope, or approach changes.
    Inno Setup
  • Run Monte Carlo simulations of random stock portfolios to identify significant factors driving Sharpe ratios. Outputs factor rankings, Pearson r, R², and scatter data for hypothesis generation.
    MIT
  • Create or update an investment thesis for a stock, including hypothesis, key price levels, risk conditions, and review dates. Overwrites existing thesis for the same stock and market.
    MIT
  • Retrieve a stock's investment thesis including hypothesis, key levels, risk conditions, watch points, tags, and next review date by providing stock ID and market.
    MIT
  • Re-tests previously-recorded findings against the current build to determine if fixes landed or regressions occurred. Classifies each finding as still present, no longer reproduces, or inconclusive.
    MIT
  • Test a single trading hypothesis by describing it in plain language. The system loads market data, charts the pattern, runs statistical tests, and provides exact entry, stop, and target prices.
    MIT
  • Search Lacuna's ML/AI corpus for papers, research directions, authors, venues, institutions, and novel hypotheses. For new research ideas, use the 'hypothesis' search type.
    MIT
  • Explore a trading topic to generate and test novel market hypotheses. Returns edge analysis, statistics, and trade setup from a comprehensive market structure knowledge graph.
    MIT
  • Track and document debugging processes with a graph-based system to decompose problems, test hypotheses, and retrieve past solutions.
    MIT