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741,830 tools. Updated 2026-11-04 09:25

"Understanding and Building Probability Density Functions (PDF) Models" matching MCP tools:

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  • A
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    Analyzes text for overused industry jargon, returning buzzword count, density score, severity level, and flagged terms. Optionally provides humorous critique in roast mode.
    230 npm
    MIT

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  • buzzword-density MCP — wraps StupidAPIs (keyless — no credential needed)

  • Publicar e rodar funções, logs, variáveis e agendamentos

  • Extract function names from PureScript code snippets, focusing solely on functions while excluding data types and classes. Ideal for quick code analysis and understanding.
    MIT
  • Lists all available indicators with typed parameters, outputs, and pane, plus allowed expression functions and syntax for building custom analyses, conditions, and hooks.
    MIT
  • Retrieve an Android device's screen resolution and display density to calculate accurate tap and swipe coordinates and map the UI layout grid.
    Apache 2.0
  • Extract 'where' clauses from PureScript functions to identify local helper functions and variables, aiding in understanding function implementation details.
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  • Calculate PDF, CDF, quantiles, mean, variance, or generate samples for supported probability distributions such as normal, exponential, Poisson, chi-squared, and more.
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  • Set value-based shading on a patterns:fill layer to vary opacity, density, or weight spatially using uniform, linear, radial, noise, or algorithm-driven functions.
    MIT
  • Calculate the probability density function for exponential distributions to analyze time-to-event data and model continuous random variables in statistical applications.
    Apache 2.0
  • Calculate probability density for normal distribution values using specified mean and standard deviation parameters.
    Apache 2.0
  • Check an audit's status, grade, and report PDF link, then download the PDF once available; request an audit first if none exists. No data is changed.
    Apache 2.0
  • Fetch a model's input schema to see required parameters, defaults, and value ranges before building a request to run inference.
    MIT
  • Compare two PDF or DOCX documents to identify changed paragraphs and table rows, word-level differences, and optional redline DOCX/PDF output.
    MIT