交互式数据可视化服务器

pn.pane.HoloViews(plot).save(buf, embed=True)

git clone https://github.com/ghostiee-11/holoviz-viz-mcp.git
cd holoviz-viz-mcp
pip install -e .
claude mcp add holoviz-viz -- holoviz-viz-mcp

bash setup.sh claude-desktop    # Claude Desktop
bash setup.sh claude-code       # Claude Code CLI
bash setup.sh cursor            # Cursor
bash setup.sh vscode            # VS Code Copilot
bash setup.sh all               # All clients at once

AI Assistant (Claude / Copilot / Cursor / any MCP client)
    |
    v  MCP Protocol (JSON-RPC 2.0 over stdio)
+------------------------------------------------------------------+
|  holoviz-viz-mcp Server (FastMCP 3.1)                             |
|                                                                   |
|  Data Layer (7 tools)        Viz Layer (11 tools)                 |
|    load_data, analyze_data     create_plot (14 chart types)       |
|    suggest_visualizations      crossfilter, streaming, datashader |
|    transform_data, merge       annotate, overlay, time_series     |
|                                                                   |
|  Intelligence Layer (5 tools)  Utility Layer (6 tools)            |
|    auto_eda                    describe_plot, clone_plot           |
|    statistical_test            get_data_sample                    |
|    data_quality_report         save/load_session                  |
|    natural_language_query      generate_large_dataset             |
|                                                                   |
|  Rendering Pipeline            State Manager                      |
|    hvPlot -> HoloViews           Versioned plots with undo        |
|    -> Panel embed=True           Dataset storage                  |
|    Output: PNG + HTML            Session persistence              |
|                                                                   |
|  8 MCP Apps  |  9 Prompts  |  3 Dashboard Templates              |
+------------------------------------------------------------------+

> "Run auto_eda on the diamonds dataset"

Returns: 6+ charts (distributions, correlations, categories, scatter),
narrative insights (skewness, outliers, strongest correlations),
all in a single tool call.

> "Test if sepal_length differs between iris species"

Returns: t-statistic, p-value, Cohen's d effect size,
box plot comparing groups, significance assessment.

# Behind the scenes:
from holoviews.selection import link_selections
linked = link_selections(hv.Layout([scatter, hist, box]))
# Brush in scatter -> histogram and box plot filter in real time

> "Decompose the weather temperature into trend, seasonal, and residual"

Returns: 4-panel decomposition plot + trend stats + seasonal amplitude.

> natural_language_query("iris", "compare sepal_length by species")

Returns structured plan:
  Step 1: transform_data('iris', 'groupby', group_by='species', agg='mean')
  Step 2: create_plot('iris_grouped', 'bar', x='species', y='sepal_length')

python demos/quick_demo.py                # Full feature tour
python demos/showcase_stock_analysis.py   # Stock prices + annotations + dashboard
python demos/showcase_ml_evaluator.py     # Feature importance + confusion matrix + crossfilter

pytest tests/ -v
# 148 tests across 16 test files covering:
# state, data, viz, transforms, crossfilter, streaming, annotations,
# export, interaction, auto-EDA, statistics, data quality, NLQ,
# big data, time series, utilities, server integration

src/holoviz_viz_mcp/
  server.py            # FastMCP entry: 36 tools, 8 resources, 9 prompts
  state.py             # Dataset + plot state with versioning/undo
  rendering.py         # HoloViews -> PNG/HTML via Panel embed (+ Material/Bootstrap/Fast templates)
  tools/
    data.py            # load, analyze, suggest, list, sample (8 datasets)
    transform.py       # filter, groupby, pivot, derive, merge
    viz.py             # create, modify, undo, list, execute_code
    crossfilter.py     # linked selections via hv.link_selections
    streaming.py       # live-updating charts with BokehJS streaming
    annotations.py     # hline, vline, spans, text, points, arrows, overlays
    dashboard.py       # layout composition with template support
    export.py          # HTML/PNG/SVG export
    interact.py        # handle_click, set_theme, launch/stop_panel
    auto_eda.py        # one-call complete exploratory analysis
    statistics.py      # t-test, correlation, regression, chi2, normality, ANOVA
    data_quality.py    # quality report + dataset comparison
    nlq.py             # natural language query interpretation
    bigdata.py         # datashader + synthetic data generation
    timeseries.py      # rolling stats, decomposition, anomaly detection
    utils.py           # describe, clone, sample, session management
  apps/
    viz.html           # Chart viewer with toolbar
    dashboard.html     # Dashboard viewer with stats
    stream.html        # Streaming viewer with pulse indicator
    crossfilter.html   # Crossfilter viewer with brush hints
    eda.html           # EDA report with tabbed insights
    statistics.html    # Statistics viewer with p-value highlights
    timeseries.html    # Time series viewer with metrics
    quality.html       # Quality report with score gauge
tests/                 # 148 tests across 16 files
demos/                 # 3 showcase scripts