Scitex Python

Name: Scitex Python
Author: ywatanabe1989

by ywatanabe1989

Python toolkit for reproducible science. CLI commands, 371+ MCP tools, built-in skills. From raw data to manuscript — with reproducibility verification. For AI and human researchers.

84 2 tools GitHub

No known CVEs

AGPL-3.0 license

Actively maintained

Last commit 2d ago

Works with most clients

Transport: stdio

2 tools · ~38 tok

Grade A · 0.0% of 200K ctx

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Education Data

Step 1

Install in your client

Config is the same across clients — only the file and path differ.

Supported in Claude Desktopstdio · Node 18+

Paste into ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "scitex-python": {
      "command": "<see-readme>",
      "args": []
    }
  }
}

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Read me

What Scitex Python does

!-- Timestamp: 2026-03-23 01:22:48 !-- File: /home/ywatanabe/proj/scitex-python/README.md

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Why this score

Five weighted categories — click any category to see the underlying evidence.

Score breakdown

70/100across 5 weighted dimensions

How we score →

0255075100

−30

Security

Maintenance

Efficiency

Documentation

Compatibility

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Security

OSV.dev

No known CVEs.

No package registry to scan.

Inventory

Tools (2)

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~38 tokens total

Community

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Frequently Asked Questions

Is Scitex Python safe to use?: Scitex Python has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments. Licensed under AGPL-3.0.
How do I install Scitex Python?: Scitex Python can be installed by cloning its GitHub repository and following the setup instructions in the README.
What can Scitex Python do?: Scitex Python provides 2 tools: Installation, Configuration. See the full tools list on the server page for descriptions and parameters.
What AI clients work with Scitex Python?: Scitex Python is compatible with claude-desktop, cursor, claude-code. It uses stdio transport.
Is Scitex Python actively maintained?: Scitex Python is actively maintained — last commit was 2 days ago. It has 84 GitHub stars.

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Community

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Problem and Solution

#	Problem	Solution
1	Fragmented tools -- literature search, statistics, figures, and writing each require separate tools with incompatible formats	Unified toolkit -- `import scitex as stx` provides 73 modules under one namespace, accessible via Python API, CLI, and MCP. These modules are standalone packages but loosely coupled through a plugin registry — each works on its own, yet composes into designed synergy (save a figure → auto-exports CSV + YAML recipe → hash-tracked by Clew → citeable in scitex-writer).
2	No verification -- existing tools address whether work could be reproduced, not whether it has been verified	Cryptographic verification -- Clew builds SHA-256 hash-chain DAGs linking every manuscript claim back to source data
3	AI agents lack context -- general-purpose LLMs cannot operate across the full research lifecycle without domain-specific tools	323 MCP tools -- AI agents run statistics, create figures, search literature, and compile manuscripts through structured tool calls
4	No custom tooling -- every lab needs domain-specific tools, but building and sharing them requires deep infrastructure knowledge	App Maker and Store -- researchers create custom apps with scitex-app SDK and share via SciTeX Cloud
5	Vendor lock-in -- cloud research tools (Overleaf, Zotero, Mendeley, Colab, GitHub Copilot) keep data on third-party servers and depend on APIs that can disappear overnight or monetize tomorrow	Open and self-hostable -- every SciTeX package is AGPL-3.0; the full 39-package ecosystem runs on your own hardware (or SciTeX Cloud which itself is self-hostable); cloud integrations are pluggable extras, not requirements

Problem

Solution

Fragmented tools -- literature search, statistics, figures, and writing each require separate tools with incompatible formats

Unified toolkit -- import scitex as stx provides 73 modules under one namespace, accessible via Python API, CLI, and MCP. These modules are standalone packages but loosely coupled through a plugin registry — each works on its own, yet composes into designed synergy (save a figure → auto-exports CSV + YAML recipe → hash-tracked by Clew → citeable in scitex-writer).

No verification -- existing tools address whether work could be reproduced, not whether it has been verified

Cryptographic verification -- Clew builds SHA-256 hash-chain DAGs linking every manuscript claim back to source data

AI agents lack context -- general-purpose LLMs cannot operate across the full research lifecycle without domain-specific tools

323 MCP tools -- AI agents run statistics, create figures, search literature, and compile manuscripts through structured tool calls

No custom tooling -- every lab needs domain-specific tools, but building and sharing them requires deep infrastructure knowledge

App Maker and Store -- researchers create custom apps with scitex-app SDK and share via SciTeX Cloud

Vendor lock-in -- cloud research tools (Overleaf, Zotero, Mendeley, Colab, GitHub Copilot) keep data on third-party servers and depend on APIs that can disappear overnight or monetize tomorrow

Open and self-hostable -- every SciTeX package is AGPL-3.0; the full 39-package ecosystem runs on your own hardware (or SciTeX Cloud which itself is self-hostable); cloud integrations are pluggable extras, not requirements

Demo — Automated Research from Data to Manuscript

40 min, minimal human intervention — an AI agent using SciTeX completed a full research cycle: literature search, statistical analysis, publication-ready figures, a 21-page manuscript, and peer review simulation. More demos are available at [https://scitex.ai/demos/](https://scitex.

Frequently Asked Questions

Is Scitex Python safe to use?

Scitex Python has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments. Licensed under AGPL-3.0.

How do I install Scitex Python?

Scitex Python can be installed by cloning its GitHub repository and following the setup instructions in the README.

What can Scitex Python do?

Scitex Python provides 2 tools: Installation, Configuration. See the full tools list on the server page for descriptions and parameters.

What AI clients work with Scitex Python?

Scitex Python is compatible with claude-desktop, cursor, claude-code. It uses stdio transport.

Is Scitex Python actively maintained?

Scitex Python is actively maintained — last commit was 2 days ago. It has 84 GitHub stars.

Problem and Solution

#	Problem	Solution
1	Fragmented tools -- literature search, statistics, figures, and writing each require separate tools with incompatible formats	Unified toolkit -- `import scitex as stx` provides 73 modules under one namespace, accessible via Python API, CLI, and MCP. These modules are standalone packages but loosely coupled through a plugin registry — each works on its own, yet composes into designed synergy (save a figure → auto-exports CSV + YAML recipe → hash-tracked by Clew → citeable in scitex-writer).
2	No verification -- existing tools address whether work could be reproduced, not whether it has been verified	Cryptographic verification -- Clew builds SHA-256 hash-chain DAGs linking every manuscript claim back to source data
3	AI agents lack context -- general-purpose LLMs cannot operate across the full research lifecycle without domain-specific tools	323 MCP tools -- AI agents run statistics, create figures, search literature, and compile manuscripts through structured tool calls
4	No custom tooling -- every lab needs domain-specific tools, but building and sharing them requires deep infrastructure knowledge	App Maker and Store -- researchers create custom apps with scitex-app SDK and share via SciTeX Cloud
5	Vendor lock-in -- cloud research tools (Overleaf, Zotero, Mendeley, Colab, GitHub Copilot) keep data on third-party servers and depend on APIs that can disappear overnight or monetize tomorrow	Open and self-hostable -- every SciTeX package is AGPL-3.0; the full 39-package ecosystem runs on your own hardware (or SciTeX Cloud which itself is self-hostable); cloud integrations are pluggable extras, not requirements