Add server: io.github.mateuslos/abstrator — recursive abstraction research MCP

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Evaluación

Dificultad
2/5
Tiempo estimado
1-3 horas
Aptitud para principiantes
55/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
docker, python
Área
release

Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

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:

  1. Decomposition — breaks the concept into simpler sub-concepts via LLM
  2. 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
  3. Bottom-up integration — synthesizes children findings into the parent, preserving conflicts and open questions
  4. 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

Lenguaje dominante
Go
Estrellas
7.4k
Forks
1k
Merge medio
17 h 29 min
PR fusionados (30 d)
68

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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