PictMCP
io.github.takeyaqaPublished by GitHub account takeyaqav0.6.0Updated Oct 24, 2026
Provides pairwise combinatorial testing capabilities to AI assistants.
Overview
A local MCP server that generates pairwise combinatorial test cases for AI assistants, using Microsoft's PICT algorithm compiled to WebAssembly.
- What you can do
- Generate optimized pairwise test cases from parameters and their values via the generate-test-cases tool, returned as structured JSON for easy integration into test design workflows.
- When to use it
- Useful when designing test cases with an AI assistant and you want deterministic, algorithm-correct pairwise coverage instead of relying on the model's combinatorial math, including cases with constraints that exclude invalid combinations.
- Requirements
- Runs locally over stdio via npx (npm package pictmcp); requires Node.js v22 or higher. No accounts, API keys, or network access needed.
Installation
In SourceWeft
- Open PictMCP in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
PictMCP
[!CAUTION] This package has been archived and will no longer be maintained. Please consider using takeyaqa/tester-skills.
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
- Node.js (v22 or higher)
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.
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:
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.
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.
Source: README.md at commit 4d2dc38
Tools
0Version history
1- v0.6.0LatestSep 16, 2026
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