MCP Framework Personal

MCP Framework Personal

A production-grade MCP server framework providing multiple specialized servers for tabular data, documents, calendars, markdown, prompt engineering, and general utilities.

Category
Visit Server

README

🧠 MCP Framework Personal

Framework de servidores MCP (Model Context Protocol) production-grade para uso local y producción.

Servidores incluidos

Servidor Descripción Tools
mcp-tabular Excel, CSV, ODS, TSV → JSON estándar 8 tools
mcp-documents PDF, Word, PPT, ODT, OCR 12 tools
mcp-calendar Días hábiles México + Divisas 14 tools
mcp-markdown Leer, analizar, transformar .md 12 tools
mcp-prompt-engineer Analizar y mejorar prompts LLM 10 tools
mcp-utils Utilidades generales (hash, regex, JSON...) 18 tools

Stack Técnico

  • SDK: mcp[cli] oficial (Anthropic) + FastMCP 2.x
  • Package manager: uv (workspaces)
  • Tipado: Pydantic v2 + type hints completos
  • Logging: structlog (JSON en prod, colorido en dev)
  • Config: pydantic-settings (12-Factor App)
  • Testing: pytest + pytest-asyncio
  • Linting: ruff + mypy
  • Docker: Multi-stage builds, non-root, healthchecks

Requisitos

  • Python 3.11+
  • uv instalado
  • Docker Desktop (para modo producción)
  • Tesseract OCR (para OCR en mcp-documents)

Instalar uv (si no lo tienes)

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Instalar Tesseract OCR (Windows)

Descargar desde: https://github.com/UB-Mannheim/tesseract/wiki

Instalador: tesseract-ocr-w64-setup-5.x.x.exe
Agregar al PATH: C:\Program Files\Tesseract-OCR\

Inicio Rápido

1. Instalar dependencias

cd c:/Users/germa/Documents/IA/mcps
uv sync --all-packages

2. Configurar variables de entorno

cp .env.example .env
# Editar .env si necesitas API keys

3. Usar con Claude Desktop

Copia el contenido de claude_desktop_config.json a tu configuración de Claude Desktop:

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Reinicia Claude Desktop. Los MCPs aparecerán automáticamente.

4. Probar un servidor (modo desarrollo)

# Iniciar servidor con hot-reload
make dev-mcp-tabular

# O usar MCP Inspector (interfaz visual)
make inspect SERVER=mcp-tabular

Comandos Makefile

make help              # Ver todos los comandos
make install           # Instalar todas las dependencias
make install-dev       # Instalar con dev tools + pre-commit
make dev-mcp-tabular   # Iniciar servidor específico en dev
make test              # Ejecutar todos los tests
make lint              # Ruff + mypy
make format            # Formatear código
make build             # Build Docker images
make up                # Levantar en Docker
make down              # Parar Docker
make logs              # Ver logs
make status            # Verificar herramientas instaladas

Estructura del Proyecto

mcps/
├── shared/                    # Librería compartida (modelos, logging, config)
├── mcp-tabular/               # Servidor archivos tabulares
├── mcp-documents/             # Servidor documentos ricos
├── mcp-calendar/              # Servidor calendario + divisas
├── mcp-markdown/              # Servidor markdown
├── mcp-prompt-engineer/       # Servidor ingeniería de prompts
├── mcp-utils/                 # Servidor utilidades
├── docker-compose.yml         # Orquestación
├── pyproject.toml             # Workspace root
├── Makefile                   # CLI operacional
├── claude_desktop_config.json # Config Claude Desktop
└── .env.example               # Variables de entorno

Despliegue en Producción (Docker)

# Copiar y configurar .env
cp .env.example .env
nano .env

# Build y levantar
make build
make up

# Verificar estado
make ps
make logs

Configuración para Claude Desktop (producción Docker)

Si usas Docker en producción, actualiza claude_desktop_config.json para usar los contenedores:

{
  "mcpServers": {
    "mcp-tabular": {
      "command": "docker",
      "args": ["run", "--rm", "-i",
               "-v", "C:/ruta/a/datos:/data:ro",
               "mcp-tabular:latest"]
    }
  }
}

Agregar Nuevos MCPs

  1. Crea la carpeta mcp-nuevo/ con la estructura estándar
  2. Agrega al workspace en pyproject.toml
  3. Agrega el servicio en docker-compose.yml
  4. Agrega la entrada en claude_desktop_config.json
  5. Agrega target en Makefile

Roadmap

  • [ ] mcp-database — SQLite + PostgreSQL + MySQL
  • [ ] mcp-web-scraper — Playwright async
  • [ ] mcp-git — Operaciones Git completas
  • [ ] mcp-search — Búsqueda semántica local (ChromaDB)
  • [ ] mcp-code-analyzer — Análisis AST Python/JS/TS
  • [ ] mcp-email — IMAP/SMTP
  • [ ] mcp-diagram — Mermaid, PlantUML, Graphviz

Soporte

Cada servidor tiene su propio README.md con documentación completa de tools y ejemplos de uso.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured