PictMCP

io.github.takeyaqa由 GitHub 账号 takeyaqa 发布v0.6.0更新于 Oct 24, 2026

Provides pairwise combinatorial testing capabilities to AI assistants.

已验证STDIO仅桌面开发者工具数据与分析

概览

AI 生成的概览

一个本地 MCP 服务器,使用编译为 WebAssembly 的 Microsoft PICT 算法,为 AI 助手生成成对组合测试用例。

可以做什么
通过 generate-test-cases 工具,根据参数及其取值生成优化的成对测试用例,并以结构化 JSON 返回,便于集成到测试设计流程中。
适用场景
适合在与 AI 助手一起设计测试用例时使用,希望获得确定且算法正确的成对覆盖,而不是依赖模型的组合数学能力,也适用于需要用约束排除无效组合的场景。
运行要求
通过 npx(npm 包 pictmcp)以 stdio 在本地运行;需要 Node.js v22 或更高版本。无需账户、API 密钥或网络访问。
安装前请注意
该包已归档且不再维护,README 建议改用其他方案。生成的测试用例不保证完全覆盖或不含缺陷,应配合其他测试策略使用。

安装

在 SourceWeft 中

  1. 打开 控制台中的 PictMCP,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

PictMCP

[!CAUTION] This package has been archived and will no longer be maintained. Please consider using takeyaqa/tester-skills.

[PictMCP Logo]

Pairwise testing for your AI assistant

PictMCP is an MCP server for software developers who design test cases with AI assistants, providing reliable, algorithm-correct pairwise test generation.

Why use this?

  • AI is great at test design, but not at combinatorial math.
  • Pairwise generation must be deterministic and correct.
  • PictMCP separates thinking (AI) from calculation (PICT).

Prefer a GUI? Check out PictRider.

Features

  • 🔒 Local Processing - All processing runs locally with no external network calls
  • ⚡ WebAssembly Powered - Fast execution using Microsoft's PICT algorithm compiled to WebAssembly
  • 🔗 Constraint Support - Define constraints to filter out invalid parameter combinations
  • 📊 Structured Output - Returns well-structured JSON results for easy integration

Installation

Prerequisites

MCP Client Configuration

Add the following configuration to your MCP client. This is an example configuration; the exact format may vary depending on your client. Please refer to your MCP client's documentation for details.

json
{  "mcpServers": {    "PictMCP": {      "command": "npx",      "args": ["-y", "pictmcp"]    }  }}

Quick Start

Once installed, you can ask your AI assistant to generate test cases using pairwise combinatorial testing.

Example Prompt

Generate test cases for a login form with the following parameters:

  • Browser: Chrome, Firefox, Safari
  • OS: Windows, macOS, Linux
  • Language: English, Japanese, Spanish

The AI assistant will use the generate-test-cases tool to create an optimized set of test cases that covers all pairwise combinations.

Example Result

AI assistants typically format the results as a table:

#BrowserOSLanguage
1ChromeLinuxJapanese
2ChromemacOSSpanish
3SafariLinuxSpanish
4FirefoxLinuxEnglish
5SafariWindowsEnglish
6FirefoxWindowsSpanish
7FirefoxmacOSJapanese
8SafarimacOSJapanese
9ChromemacOSEnglish
10ChromeWindowsJapanese

Example with Constraints

Generate test cases for:

  • Browser: Chrome, Firefox, Safari
  • OS: Windows, macOS, Linux
  • Language: English, Japanese, Spanish

With constraint: Safari only works on macOS

You can describe constraints in plain language — the AI assistant will convert them into PICT constraint syntax automatically.

#BrowserOSLanguage
1FirefoxLinuxSpanish
2ChromeWindowsSpanish
3FirefoxWindowsJapanese
4ChromeLinuxJapanese
5ChromemacOSEnglish
6FirefoxWindowsEnglish
7ChromeLinuxEnglish
8SafarimacOSSpanish
9SafarimacOSJapanese
10FirefoxmacOSSpanish
11SafarimacOSEnglish

FAQ

Does this communicate with external servers?

No. All processing runs locally with no external network calls.

I already use the pict CLI. Do I need this?

If your AI agent can execute CLI commands directly, you may not need this tool. However, PictMCP provides:

  • A standardized MCP interface for AI assistants
  • No need to install PICT separately (WebAssembly-based)
  • Structured JSON output instead of TSV

What is pairwise testing?

Pairwise testing (also known as all-pairs testing) is a combinatorial testing method that generates test cases covering all possible pairs of input parameters. This significantly reduces the number of test cases while maintaining high defect detection rates.

What constraint syntax is supported?

You don't need to write PICT syntax directly. Simply describe constraints in natural language and your AI assistant will handle the conversion. PictMCP supports the full PICT constraint syntax. See the PICT documentation for details.

License

This project is licensed under the MIT License—see the LICENSE file for details.

Disclaimer

PictMCP is provided "as is", without warranty of any kind. The authors are not liable for any damages arising from its use.

Generated test cases do not guarantee complete coverage or the absence of defects. Please supplement pairwise testing with other strategies as appropriate.

PictMCP is an independent project and is not affiliated with Microsoft Corporation.


If you find PictMCP useful, please consider starring the repository.

来源:README.md,提交 4d2dc38

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.6.0最新Sep 16, 2026

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