An advanced quality control data analysis MCP server that automates SPC control charts, process capability indices, and Weibull reliability reports directly from raw data.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install qc-data-processor
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install qc-data-processor using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is QC Data Processor?
The QC Data Processor is a robust Model Context Protocol (MCP) server engineered to automate quality control analysis and lifetime reliability modeling. Built for modern AI-driven manufacturing and engineering workflows, this tool bridges the gap between raw CSV/Excel datasets and actionable quality insights. It enables AI agents to instantly parse measurement files, detect appropriate statistical pipelines, and output production-ready metrics and reports.
By integrating this server with Openclaw Skills, developers and quality engineers can build workflows that calculate standard industry metrics dynamically. The server leverages mathematical algorithms matching industry-standard desktop packages like Minitab and JMP, providing robust mathematical verification without proprietary GUI software. It operates entirely over stdin/stdout, making it highly secure, performant, and easy to deploy in any local or cloud-based environment.
QC Data Processor Use Cases
- Automated SPC Charting: Instantly calculate control limits and detect anomalies using Western Electric rules on production measurement data.
- Process Capability Evaluation: Analyze manufacturing stability and compute process capability indices (Cp, Cpk, Pp, Ppk) to ensure parts meet tight tolerances.
- Life Test Reliability Analysis: Fit life test or time-to-failure data to Weibull, Lognormal, or Exponential distributions via Maximum Likelihood Estimation (MLE) to calculate B10 life and MTTF.
- AI-Generated QC Reports: Generate comprehensive quality reports (Daily, Weekly, 8D, or Reliability) in Markdown format directly from raw statistical analysis outputs.
How QC Data Processor Works
- Data Ingestion: The AI agent passes a file path (.csv, .xlsx, .xls) to the parser tool, which automatically identifies column types, analyzes pipelines, and suggests control charts.
- Statistical Analysis: Depending on the user's requirements, the agent executes SPC metrics (e.g., Xbar-R, I-MR charts) or performs distribution fitting (e.g., Weibull) to find the best-fit model based on AICc values.
- Result Compilation: The analysis tools compile mathematical statistics, control limits, B-life metrics, and Western Electric rule violation warnings into structured JSON outputs.
- Report Generation: The compiled metrics are funneled into a markdown report generator using pre-defined templates (daily, weekly, 8D, or reliability) to produce clear, executive-ready documentation.
QC Data Processor Setup
Prerequisites
Ensure you have Python installed alongside the necessary dependencies. This tool uses stdio transport and does not require external API keys.
1. Installation of Dependencies
Install the required Python packages:
pip install mcp pandas openpyxl numpy scipy reliability
2. Configure MCP Server
Add the following configuration to your host system or AI assistant settings (such as Claude Desktop or your Openclaw Skills configuration files):
{
"mcpServers": {
"qc-data-processor": {
"command": "python",
"args": ["path/to/mcp_server.py"],
"env": {}
}
}
}
QC Data Processor Data Schema & Taxonomy
Tool Interface Specifications
| Tool Name | Input Parameters | Output Description |
|---|---|---|
qc_parse_data_tool |
file_path (str), mode (str, optional) |
JSON schema outlining column types, process capability pipelines, and suggested chart types. |
qc_spc_analyze_tool |
data_schema (dict), column (str), subgroup_size (int, optional), chart_type (str, optional) |
SPC statistics (control limits, capability indices like Cp/Cpk), Western Electric rule alarms, and plotting coordinates. |
qc_reliability_analyze_tool |
data_schema (dict), time_column (str), censor_column (str), distribution (str, optional) |
Best-fit reliability distribution parameters, B10/B50/MTTF calculations, and probability plotting arrays. |
qc_report_generate_tool |
analysis_result (dict), template (str), metadata (dict) |
A fully structured report written in clean Markdown format (Daily, Weekly, 8D, or Reliability). |
QC Data Processor Advanced Features
- Minitab & JMP Algorithmic Alignment: All calculated statistical metrics, control limit factors, and capability indices precisely match standard algorithms found in legacy QC desktop software.
- Western Electric Rules Engine: Built-in logic checks SPC datasets against multi-point instability patterns to alert agents about process drift or out-of-control conditions.
- Auto-Distribution Fitting: Employs AICc (Akaike Information Criterion with Correction) to compare and automatically select the most accurate reliability distribution among Weibull, Lognormal, and Exponential models.
- Native Openclaw Skills Support: Optimized to be orchestrated by multi-agent developer workflows, letting LLMs loop through raw files, analysis nodes, and automated reporting pipelines autonomously.
SKILL.md
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