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  3. Bayescausal
MCP server · Healthcare

Bayescausal

· github.com/davccavalcante/bayescausal

Calibrated incident root cause inference for TypeScript: declarative Bayesian networks with exact and approximate inference and the do-operator, zero runtime dependencies.

Unclaimed anthropicbayesianbayesian-networkbelief-propagationcalibrationcausal-inferencediagnosisdo-operatoredgeincident-responsellmmcpnodejsobservabilityopenaiprobabilistic-graphical-modelroot-cause-analysissretypescriptzero-dependency
InstallView source
34Low

Scored 8 hours ago · breakdown

About Bayescausal

Bayescausal is an MCP server in the Healthcare category: calibrated incident root cause inference for TypeScript: declarative Bayesian networks with exact and approximate inference and the do-operator, zero runtime dependencies. It has been installed 0 times through Conduid.

Install

Clone
git clone https://github.com/davccavalcante/bayescausal

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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Security checks

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  • ·Dependencies pinnedNot checked yet.
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Questions

About Bayescausal

How do I install Bayescausal?

Run git clone https://github.com/davccavalcante/bayescausal, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is Bayescausal safe to use with an AI agent?

Its trust score is 34 out of 100 (low). Conduid hasn't run static security checks on this repository yet, so review the source yourself before granting it credentials. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Bayescausal still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.