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Math & Statistics Graphing Engine

借助远程 MCP 数学与统计绘图引擎,绘制函数、点、线段、标签、三角形和柱状等元素,自动求根、极值与交点;并提供描述统计、直方图、回归、分布拟合和假设检验,结果以可交互图形呈现,用户可缩放、平移和调整参数。

architectds
architectds
热度 72.63
最近更新 2026-05-11 17:35:35
分类:数据依赖:MCP 服务应用场景:数据处理 / 研究

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add mathgraphs
Metadata
name
mathgraphs
version
1.0.0
description
Math & statistics graphing, computation, visualization and validation engine
author
MathTalking
homepage
https://mathtalking.com
mcp_servers
[{"url":"https://mathtalking.com/api/mcp","transport":"streamable-http"}]
tags
["math","graphing","statistics","visualization","education","plot","geometry"]

Math & Statistics Graphing Engine

You have access to an interactive math and statistics graphing engine via MCP. It computes and renders results — roots, extrema, intersections, regression, hypothesis tests — on interactive graphs.

When to use this skill

  • User asks to graph, plot, or visualize any math
  • User needs to verify a mathematical result visually
  • You computed an answer and want to show it, not just describe it
  • Data needs statistical visualization (histogram, regression, distribution fit)
  • Geometry needs precise rendering (triangles, circles, constructions)

Tools

plot_graph — Math Visualization

Plot functions, points, segments, labels, and shapes. Auto-computes roots, extrema, and intersections.

Element types:

  • function: expression like "x^2-4", "sin(x)", "x^2+y^2=1", "(cos(t),sin(t))"
  • points: array of {x, y} coordinates with optional label
  • segment: line from (x1,y1) to (x2,y2) with optional arrow/dashed
  • label: text at position (x, y)
  • triangle: three vertices (x1,y1,x2,y2,x3,y3)
  • box: edge + height for bar charts

compute_stats — Descriptive Statistics

Input: array of numbers. Returns mean, median, std, min, max, quartiles.

add_histogram — Histogram

Input: array of numbers. Auto-bins and draws bars.

add_regression — Regression

Input: array of {x,y} points. Fits linear/quadratic/exponential/power. Returns R².

fit_distribution — Distribution Fitting

Input: array of numbers. Fits normal/uniform/exponential. Returns best fit.

test_hypothesis — Hypothesis Test

Input: data groups + test type. Returns p-value with visual rejection region.

Important

  • All tools return an interactive URL — always share it with the user
  • The graph is live: user can zoom, pan, add functions, adjust sliders
  • Results are computed from the graph, not generated — no hallucinated curves
  • Supports 9 languages: en, zh, zh-TW, ja, ko, es, fr, de, pt-BR