Add server: io.github.mateuslos/abstrator — recursive abstraction research MCP
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调研方向
Start by reading the registry's server submission requirements and checking how existing PyPI server submissions are represented; the issue provides a server.json proposal and ownership marker but names no registry files or tests. Confirm the submission meets the registry's validation requirements, then run the relevant validation or submission checks. Done means the registry accepts this server entry.
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描述
Server submission
Package name: io.github.mateuslos/abstrator
PyPI: https://pypi.org/project/abstrator/0.1.0/
Repository: https://github.com/MateusLOS/abstrator
License: MIT
server.json
{
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"name": "io.github.mateuslos/abstrator",
"title": "Abstrator",
"description": "Recursive abstraction research — decomposes any topic into sub-concepts, researches each through 8 specialized lenses (meaning, theory, practice, best_practices, expert_views, advanced_methods, existing_tools, analogy), and synthesizes a professional-grade guide.",
"version": "0.1.0",
"packages": [
{
"registryType": "pypi",
"identifier": "abstrator",
"version": "0.1.0",
"transport": {
"type": "stdio",
"command": "abstrator",
"args": ["mcp"]
}
}
],
"repository": {
"url": "https://github.com/MateusLOS/abstrator"
},
"license": "MIT"
}
What it does
Abstrator is an MCP server + CLI that takes any question or topic and runs a recursive research chain:
- Decomposition — breaks the concept into simpler sub-concepts via LLM
- Lens-based research — each sub-concept is researched through 8 named lenses (
meaning,theory,practice,best_practices,expert_views,advanced_methods,existing_tools,analogy) using local SearXNG + page fetching - Bottom-up integration — synthesizes children findings into the parent, preserving conflicts and open questions
- Root synthesis — produces a professional-grade markdown guide with best practices, expert views, methodologies and tools
Primary tool: ask
ask(query: str, mode: "quick"|"standard"|"deep") -> str
Returns clean markdown ready to use in any AI conversation. No configuration needed with claude_code_cli provider (uses Claude Code OAuth).
Quick start
pip install abstrator
docker run -d -p 8888:8080 searxng/searxng
abstrator setup
claude mcp add abstrator -- abstrator mcp
Ownership verification
The <!-- mcp-name: io.github.mateuslos/abstrator --> marker is present in the README: https://github.com/MateusLOS/abstrator/blob/main/README.md
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环境准备
- 提供 Dockerfile 或 Docker Compose 文件
- 没有 Pull Request 模板
- 阅读贡献指南
从这里开始
- 先读完整个 Issue，再读项目的贡献指南。
- 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
- Fork 仓库，在一个分支上完成修改。
- 提交 Pull Request，并在描述里引用这个 Issue 编号。
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