Datyra MCP
Permite ejecutar consultas SQL directamente sobre bases de datos DuckDB para el análisis de datos.
Integra Jupyter como motor de análisis y soporta la lectura de cuadernos de Jupyter para su procesamiento y modelado.
Permite ejecutar consultas SQL directamente sobre bases de datos SQLite para el análisis de datos.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Datyra MCPanalyze sales data and generate a dashboard"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
◈ 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.
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 |
| Natural language → correct engine |
|
|
| — |
| Pearson/Spearman/Shapiro/IQR |
|
| RF regression/classification + KMeans |
|
| 4 MOD PNGs 180dpi + HTML |
|
| All CSVs batch |
|
| Preview + describe |
|
| SQL over sqlite/duckdb |
|
| Available engines | — |
| 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 # inspectorExtra: profile_data + query_sql + get_tool_status as plus features.
3. Visual demo

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_MODClassification 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 rapidorequirements.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 connectedSession only: create
opencode.jsononly inC:\ruta\proyecto\opencode.jsonand 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 verbose6.2 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 |
|
Cybersecurity | 20k |
|
Finance | 20k |
|
Sales | 25 |
|
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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