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glama

data-science-mcp

Enables time-series analysis and forecasting through a structured tool catalogue, including data loading, quality repair, diagnostics, and forecasting with ARIMA, exponential smoothing, Chronos-2, Toto 2.0, and AutoML.

→ LiChenStuttgart/data-science-mcp· listed on glama
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// key findings
✅
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
✅
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
// required environment variables
This server reads these from process.env. You'll be asked to provide them before it can run.
configTS_MCP_MODEL_DEVICE— the canonical model. Set to cpu, cuda, or another
configTS_MCP_MODEL_CACHE_DIR
configTS_MCP_MODEL_WEIGHTS_DIR— supported device value. Set to use a different
configTS_MCP_CHRONOS2_MODEL_ID
configTS_MCP_TOTO2_MODEL_ID
configMPLCONFIGDIR
// full audit trail
The full breakdown of what we checked, the deductions that landed, the network hosts, the dependency advisories, and concrete fix guidance is available to verified publishers.
// improvement guidance — verified publishers only
We have 2 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
[![M8ven Score](https://m8ven.ai/badge/mcp/lichenstuttgart-data-science-mcp-0xuuuz)](https://m8ven.ai/mcp/lichenstuttgart-data-science-mcp-0xuuuz)
commit: cd9ce31845d7e8fa199a3c6ac85a30c97d7a4af9
code hash: 41f69605910b92a751e448c9dc075ceeae194b16e38b7113a99fd42ebcb0801d
verified: 7/21/2026, 8:41:46 AM
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