What is the STAMP MCP server?

KatherLab/STAMP — Solid Tumor Associative Modeling in Pathology AgentRank score: 66.4/100.

Is the STAMP MCP server actively maintained?

The STAMP MCP server was last committed 2mo ago and has 17 contributors. Issue health: 77% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

STAMP MCP Server

Name: KatherLab/STAMP
Rating: 3.7 (1 reviews)
Author: KatherLab

KatherLab/STAMP

Score: 66.4 Rank #4798 MCP Server

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Solid Tumor Associative Modeling in Pathology

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Overview

KatherLab/STAMP is a Python tool licensed under MIT. Solid Tumor Associative Modeling in Pathology Topics: bioimage-analysis, bioinformatics, deep-learning, digital-pathology, heatmaps-visualization-tool, histopathology, pathology, pathology-informatics, research, tcga-data, weakly-supervised-learning, whole-slide-imaging, agents, mcp, mcp-server, survival-analysis.

Ranked #4798 out of 24846 indexed tools.

Has 17 contributors.

Ecosystem

Python MIT

bioimage-analysisbioinformaticsdeep-learningdigital-pathologyheatmaps-visualization-toolhistopathologypathologypathology-informaticsresearchtcga-dataweakly-supervised-learningwhole-slide-imagingagentsmcpmcp-serversurvival-analysis

Score Breakdown

Stars 15% 131

131 stars → growing community

Freshness 25% 2mo ago

Last commit 2mo ago → moderate activity

Issue Health 25% 77%

47/61 issues closed → decent issue management

Contributors 10% 17

17 contributors → active community

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 60

Description Brief

License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

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Freshness high impact

Last commit was 82 days ago — a recent commit would boost your freshness score

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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Details

Stars: 131
Forks: 60
Open Issues: 14
Closed Issues: 47
Contributors: 17
Dependents: —
Language: Python
License: MIT
Last Commit: 2mo ago

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From the README

# STAMP: A Protocol for Solid Tumor Associative Modeling in Pathology

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*An efficient, ready‑to‑use workflow from whole‑slide image to biomarker prediction.*

STAMP is an **end‑to‑end, weakly‑supervised deep‑learning pipeline** that helps discover and evaluate candidate image‑based biomarkers from gigapixel histopathology slides, no pixel‑level annotations required. Backed by a peer‑reviewed protocol and used in multi‑center studies across several tumor types, STAMP lets clinical researchers and machine‑learning engineers collaborate on reproducible computational‑pathology projects with a clear, structured workflow.

**Want to start now?** [Jump to Installation](#installation) or [walk through our Getting Started guide](getting-started.md) for a hands-on tutorial.

## **Why choose STAMP?**

* 🚀 **Scalable**: Run locally or on HPC (SLURM) with the same CLI; built to handle multi‑center cohorts and large WSI collections.  
* 🎓 **Beginner‑friendly & expert‑ready**: Zero‑code CLI

Read full README on GitHub →