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Add MseeP.ai badge#6

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Add MseeP.ai badge#6
lwsinclair wants to merge 1 commit intomims-harvard:mainfrom
lwsinclair:add-mseep-badge-a162765c

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@lwsinclair
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Hi there,

This pull request shares a security update on ToolUniverse.

We also have an entry for ToolUniverse in our directory, MseeP.ai, where we provide regular security and trust updates on your app.

We invite you to add our badge for your MCP server to your README to help your users learn from a third party that provides ongoing validation of ToolUniverse.

You can easily take control over your listing for free: visit it at https://mseep.ai/app/mims-harvard-tooluniverse.

Yours Sincerely,

Lawrence W. Sinclair
CEO/SkyDeck AI
Founder of MseeP.ai
MCP servers you can trust


MseeP.ai Security Assessment Badge

Here are our latest evaluation results of ToolUniverse

Security Scan Results

Security Score: 100/100

Risk Level: low

Scan Date: 2025-06-13

Score starts at 100, deducts points for security issues, and adds points for security best practices

This security assessment was conducted by MseeP.ai, an independent security validation service for MCP servers. Visit our website to learn more about our security reviews.

gasvn added a commit that referenced this pull request Feb 27, 2026
…nk tolerance

drug_synergy_tool.py — Bug #7 (CRITICAL): ZIP fallback was Bliss, not ZIP
- ZIP requires Hill curve fitting; without it a Bliss independence surface was
  silently returned labelled as a ZIP delta score — scientifically wrong
- Now returns a clear error naming the drug(s) that failed Hill fitting and
  directing the user to calculate_bliss for a non-parametric alternative

drug_synergy_tool.py — Bug #8: Emax upper bound 1.5 in Loewe/CI Hill fitter
- _fit_hill_for_loewe uses fractional inhibition (0-1); Emax > 1 is physically
  impossible, yet the scipy curve_fit upper bound allowed 1.5
- Corrected to 1.0 (Loewe & Muischnek 1926; Chou & Talalay 1984)

nca_tool.py — Bug #4: terminal slope accepted 2 instead of 3 post-Tmax points
- FDA NCA guidance requires 3+ points for lambda_z: a 2-point linear regression
  is a perfect fit by construction (R2=1 trivially), giving no quality signal
- Now requires 3+ post-Tmax positive concentrations before using the full set;
  falls back to last n_points otherwise

survival_tool.py — Bug #6: log-rank zero-variance check used absolute tolerance
- Threshold 1e-10 too tight for large studies: float rounding errors over many
  event times scale as N x 2.2e-16, so 1e-10 would erroneously reject valid
  proportional distributions in datasets with thousands of events
- Replaced with relative tolerance: 1e-8 x total_events (safe up to 10^7 events)
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