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◈ Datyra MCP — AI Context Mediator

Author: Nakano-04 — Mercenary Company "Mercenario del rojo" · Software Engineer (Cybersecurity · AI · Systems · ERP) · Building security tools, AI systems, ERP modules & research · ✉️ nakano.04.2025@gmail.com · 🔗 https://github.com/Nakano-04

Intelligent orchestrator + real universal MCP Server that understands natural language and routes automatically: Python · R · SQL · Spark · Jupyter · Power BI · Tableau — compatible with any AI that speaks MCP.

version python mcp style license


1. What is it?

CLI mediator + HTML MOD + MCP Server that analyzes any file in Recursos/ without code. You say:

"analiza las ventas"
"genera dashboard con gráficos"
"predice moroso con machine learning"
"haz analisis estadistico correlacion"

For humans → CLI. For AIs → MCP tools (Claude, Devin, Codex, opencode, Cursor, Windsurf, Copilot, Gemini, ChatGPT and any other with MCP — open protocol).


Related MCP server: mix_server

2. MCP Server — 10 tools (universal)

mcp_server.py (FastMCP, stdio + http) exposes full cycle 1-6 — universal for any AI:

Tool

Description

Example

analyze_data

Natural language → correct engine

analyze cybersecurity

list_datasets

Recursos/ inventory

run_statistics

Pearson/Spearman/Shapiro/IQR

correlate amount vs score

train_model

RF regression/classification + KMeans

predict delinquent

generate_dashboard

4 MOD PNGs 180dpi + HTML

mining dashboard

batch_process

All CSVs batch

all batch

profile_data

Preview + describe

finanzas_masivo.csv

query_sql

SQL over sqlite/duckdb

SELECT region, SUM(ingreso)...

get_tool_status

Available engines

author_info

Nakano-04 / Mercenary Company info

Universal benefits (any AI with MCP):

  • Devin: direct analysis, reports in workflow, auto logs/metrics

  • Claude: complex stats, dashboards, massive Spark

  • Codex/opencode/Cursor/Windsurf/Copilot: pattern-based suggestions, autocomplete, generates DS code

  • Gemini/ChatGPT/others: same mcp.json — standard protocol, no vendor lock-in

Universal configuration

Any AI with MCP — Claude Desktop, opencode, Cursor, Windsurf, VS Code, Windsurf, Copilot, Gemini CLI, etc. (%APPDATA%\Claude\claude_desktop_config.json / mcp.json / .cursor/mcp.json):

{
  "mcpServers": {
    "datyra-mcp": {
      "command": "py",
      "args": ["C:/ruta/a/Mediador-Analisis-de-Datos/mcp_server.py"]
    }
  }
}

See included mcp.json and .opencode.json.example.

py mcp_server.py              # stdio (Claude/opencode)
py -m mcp dev mcp_server.py   # inspector

Extra: profile_data + query_sql + get_tool_status as plus features.


3. Visual demo

Demo GIF

CLI with Rich: cyan/magenta tables, panels, dots12 spinner. HTML MOD report salida/informe_*.html: gradient header #0a0e14→#1a1033, neon badges, base64 gallery. MOD charts visual_motor.py: KDE histogram, horizontal bars, annotated heatmap, time series — 180dpi, seaborn, background #151a23, neon palette.


4. Architecture

mediador_ia.py → mediador/nucleo.py → clasificar() → MOTOR_POR_CLAVE → motor.ejecutar() → informe.py → HTML+TXT
mcp_server.py  →  FastMCP 9 tools  →  nucleo/contexto  (mismo core)

motores/: python_motor (1-6), r_motor, sql_motor, spark_motor, jupyter_motor, visual_motor MOD
contexto.py: rglob Recursos/, tipos, filas
herramientas.py: detección binarios
estilos.py: PALETA_MOD, CSS_MOD

Classification nucleo.py:22 by keywords + ORDEN_PRECEDENCIA: ml > big_data > sql > jupyter > visual > estadistica > r > python.


5. Installation

5.1 Requirements

Python 3.10+ + pip

5.2 Base installation (CLI)

cd Mediador-Analisis-de-Datos
py -m venv .venv; .venv\Scripts\activate  # Windows
# Linux/Mac: python3 -m venv .venv; source .venv/bin/activate
pip install -r requirements.txt
pip install -r requirements-extra.txt  # opcional: jupyter/pyspark
py mediador_ia.py -t; py mediador_ia.py -l
py mediador_ia.py "analiza las ventas"  # test rapido

requirements.txt: pandas, openpyxl, matplotlib, seaborn, scipy, scikit-learn, rich, jinja2, mcp, anyio, httpx, uvicorn, starlette

5.3 Installation with opencode (MCP)

npm install -g opencode-ai@latest
opencode --version  # verificar 1.18.22+

Configure MCP — create opencode.json in your project or global ~/.config/opencode/opencode.jsonc:

{
  "mcp": {
    "datyra-mcp": {
      "type": "local",
      "command": ["py", "C:/ruta/a/Mediador-Analisis-de-Datos/mcp_server.py"],
      "enabled": true
    }
  }
}

See included .opencode.json.example and mcp.json. Then:

opencode mcp list  # debe salir ✓ datyra-mcp connected

Session only: create opencode.json only in C:\ruta\proyecto\opencode.json and delete it when done.


6. Usage

6.1 Direct CLI

py mediador_ia.py "analiza las ventas"
py mediador_ia.py "genera dashboard de mineria con graficos"
py mediador_ia.py "predice moroso con machine learning en finanzas"
py mediador_ia.py "analisis estadistico correlacion"
py mediador_ia.py "analiza todos batch"  # batch todos los CSVs
py mediador_ia.py                        # REPL: -l, -t, !cmd, salir
# flags: -l lista datasets, -t test motores, -o salida/, -v verbose

6.2 With opencode (live MCP)

opencode                          # abre TUI
# dentro de opencode escribe:
"analiza finanzas_masivo 20k filas: limpia IQR/nulos, EDA skew+Pearson, RF150 moroso, dashboard 4 graficos"
# o headless:
opencode run "analiza ventas con dashboard 4 graficos"

MCP flow: opencode → datyra-mcp → nucleo.clasificar() → MOTOR_POR_CLAVE → visual_motor MOD → salida/informe_*.html Then open salida/informe_*.html in Edge/Chrome (--app without tabs) and salida/*.png 180dpi.

6.3 MCP tools (from any AI)

analyze_data, list_datasets, run_statistics, train_model, generate_dashboard, batch_process, profile_data, query_sql, get_tool_status, author_info

7. Included massive datasets (test)

Dataset

Rows

File

Mining

15k

Recursos/mineria_masivo.csv

Cybersecurity

20k

Recursos/ciberseguridad_masivo.csv

Finance

20k

Recursos/finanzas_masivo.csv

Sales

25

Recursos/ventas_ejemplo.csv

Supported formats: csv/tsv/csv.gz, xlsx/xls, json, parquet, db/sqlite/duckdb, ipynb, pbix, twb/twbx, r/rds, sql, pkl. Priority csv>excel>parquet>json or name match.


8. Engines

Python (1-6): duplicates, null %, IQR, skew, train_test_split, RF 150 MAE/RMSE/R2/CV, KMeans k=3/4/5, Pearson/Spearman/Shapiro. Visual MOD: KDE, horizontal bars, heatmap, time series. R: dplyr, summary, cor, shapiro, kmeans. SQL/Spark/Jupyter/PBI/Tableau: detect binary or fallback.

9. salida/ output

analisis_*.py, graficos_*.py, *.png (180dpi), informe_*.txt, informe_*.html (dark, base64)

10. Data Science 1-6 (no cross-cutting)

Stage

Covered

1 Definition

Automatic classification

2 Collection

Multi-format inventory

3 Cleaning

duplicates, nulls, IQR, datetime

4 EDA

describe+skew+cor+4 MOD charts

5 Modeling

RF+KMeans+Pearson/Spearman/Shapiro

6 Evaluation

metrics, train/test, HTML

No cross-cutting block (governance/ethics) on request.

11. Roadmap

  • Rich CLI + HTML MOD + 180dpi

  • IQR cleaning + evaluated ML + KMeans + Shapiro

  • Batch + MCP Server 10 tools

  • 35s live opencode demo GIF + docs

  • Streamlit drag&drop

  • ARIMA/Prophet

12. Author

Nakano-04 — Mercenary Company "Mercenario del rojo" Software Engineer (Cybersecurity · AI · Systems · ERP) Building security tools, AI systems, ERP modules & research 📧 nakano.04.2025@gmail.com · 🔗 https://github.com/Nakano-04

13. License

Apache 2.0 — see LICENSE.

Made with pandas · seaborn · rich · jinja2 · mcp MOD dark style — Datyra MCP by Nakano-04.

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