MCP server exposing shewhart's statistical process control analyses as tools for AI agents.
Project description
shewhart-mcp
An MCP server that exposes shewhart, the statistical process control library for Python, as tools for AI agents.
The reasoning: agents asked about process data tend to generate their own statistics, and generated statistics fail silently. Calculation belongs in a deterministic, versioned, validated tool that the agent calls. The agent interprets; validated code calculates.
Status
In development. This release reserves the package name; the first working server ships shortly, with these tools:
| Tool | Purpose |
|---|---|
review |
one call: choose the right chart, check assumptions, return a structured verdict |
control_chart |
I-MR, Xbar-R/S, p/np/c/u, Laney, EWMA, CUSUM by alias |
capability |
Cp/Cpk/Pp/Ppk with confidence intervals, non-normal methods |
tolerance_interval |
normal (Howe k2) and nonparametric (Wilks) |
fit_baseline / evaluate_baseline |
freeze control limits, judge new data against them |
Every result carries provenance (library version, input hash, timestamp) and is validated against published reference values, including NIST-certified datasets, in CI.
License
MIT. Built by Bertan Ucar.
Project details
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The following attestation bundles were made for shewhart_mcp-0.0.1-py3-none-any.whl:
Publisher:
release.yml on bertanucar/shewhart-mcp
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https://in-toto.io/Statement/v1 -
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bertanucar/shewhart-mcp@e08494ac970d0e304cb8ca1937b3b11179423a5b -
Branch / Tag:
refs/tags/v0.0.1 - Owner: https://github.com/bertanucar
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https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
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release.yml@e08494ac970d0e304cb8ca1937b3b11179423a5b -
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push
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