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Why MCP Plots?

• Instant, visual-first charts using Mermaid (renders directly in MCP clients like Cursor)

• Simple prompts to generate charts from plain data

• Zero-setup options via uvx, or install from PyPI/Docker

• Flexible output formats: mermaid (default), PNG image, or text

Related MCP server: MCP CSV Analysis with Gemini AI

Quick Usage

• Ask your MCP client: "Create a bar chart showing sales: A=100, B=150, C=80"

• Default output is Mermaid, so diagrams render instantly in Cursor

Quick Start

PyPI Installation (Recommended)

pip install mcp-plots
mcp-plots # Start the server

For Cursor Users

1. Install the package: pip install mcp-plots

2. Add to your Cursor MCP config (~/.cursor/mcp.json):

   {
    "mcpServers": {
    "plots": {
    "command": "mcp-plots",
    "args": ["--transport", "stdio"]
    }
    }
   }

   Alternative (zero-install via uvx + PyPI):

   {
    "mcpServers": {
    "plots": {
    "command": "uvx",
    "args": ["mcp-plots", "--transport", "stdio"]
    }
    }
   }

3. Restart Cursor

4. Ask: "Create a bar chart showing sales: A=100, B=150, C=80"

Development Installation

uvx --from git+https://github.com/mr901/mcp-plots.git run-server.py

Documentation → | Quick Start → | API Reference →

MCP Registry

This server is published under the MCP registry identifier io.github.MR901/mcp-plots. You can discover/verify it via the official registry API:

curl "https://registry.modelcontextprotocol.io/v0/servers?search=io.github.MR901/mcp-plots"

Registry metadata for this project is tracked in server.json.

Install with Smithery

This repository includes a smithery.yaml for easy setup with Smithery.

• File: smithery.yaml

• Docs: https://smithery.ai/docs/config#smitheryyaml

Example install using the Smithery CLI (adjust --client as needed, e.g. cursor, claude):

npx -y @smithery/cli install \
 https://raw.githubusercontent.com/mr901/mcp-plots/main/smithery.yaml \
 --client cursor

After installation, your MCP client should be able to start the server over stdio using the command defined in smithery.yaml.

Project layout

src/
 app/ # Server construction and runtime
 server.py
 capabilities/ # MCP tools and prompts
 tools.py
 prompts.py
 visualization/ # Plotting engines and configurations
 chart_config.py
 generator.py

Requirements

• Python 3.10+

• See requirements.txt

Setup Routes

uvx (Recommended)

The easiest way to run the MCP server without managing Python environments:

# Run directly with uvx (no installation needed)
uvx --from git+https://github.com/mr901/mcp-plots.git run-server.py

# Or install and run the command
uvx --from git+https://github.com/mr901/mcp-plots.git mcp-plots

# With custom options
uvx --from git+https://github.com/mr901/mcp-plots.git mcp-plots --port 8080 --log-level DEBUG

Why uvx?

• No Environment Management: Automatically handles Python dependencies

• Isolated Execution: Runs in its own virtual environment

• Always Latest: Pulls fresh code from repository

• Zero Setup: Works immediately without pip install

• Cross-Platform: Same command works on Windows, macOS, Linux

PyPI (Traditional Installation)

1. Install dependencies

pip install -r requirements.txt

2. Run the server (HTTP transport, default port 8000)

python -m src --transport streamable-http --host 0.0.0.0 --port 8000 --log-level INFO

3. Run with stdio (for MCP clients that spawn processes)

python -m src --transport stdio

Local Development (from source)

git clone https://github.com/mr901/mcp-plots.git
cd mcp-plots
pip install -e .
python -m src --transport stdio --log-level DEBUG

Docker

docker build -t mcp-plots .
docker run -p 8000:8000 mcp-plots

Environment variables (optional):

• MCP_TRANSPORT (streamable-http|stdio)

• MCP_HOST (default 0.0.0.0)

• MCP_PORT (default 8000)

• LOG_LEVEL (default INFO)

Tools

• list_chart_types() → returns available chart types

• list_themes() → returns available themes

• suggest_fields(sample_rows) → suggests field roles based on data samples

• render_chart(chart_type, data, field_map, config_overrides?, options?, output_format?) → returns MCP content

• generate_test_image() → generates a test image (red circle) to verify MCP image support

Cursor Integration

This MCP server is fully compatible with Cursor's image support! When you use the render_chart tool:

• Charts appear directly in chat - No need to save files or open separate windows

• AI can analyze your charts - Vision-enabled models can discuss and interpret your visualizations

• Perfect MCP format - Uses the exact base64 PNG format that Cursor expects

The server returns images in the MCP format Cursor requires:

{
 "content": [
 {
 "type": "image", 
 "data": "<base64-encoded-png>",
 "mimeType": "image/png"
 }
 ]
}

Example call (pseudo):

render_chart(
 chart_type="bar",
 data=[{"category":"A","value":10},{"category":"B","value":20}],
 field_map={"category_field":"category","value_field":"value"},
 config_overrides={"title":"Example Bar","width":800,"height":600,"output_format":"MCP_IMAGE"}
)

Return shape (PNG):

{
 "status": "success",
 "content": [{"type":"image","data":"<base64>","mimeType":"image/png"}]
}

Configuration

The server can be configured via environment variables or command line arguments:

Server Settings

• MCP_TRANSPORT - Transport type: streamable-http or stdio (default: streamable-http)

• MCP_HOST - Host address (default: 0.0.0.0)

• MCP_PORT - Port number (default: 8000)

• LOG_LEVEL - Logging level: DEBUG, INFO, WARNING, ERROR, CRITICAL (default: INFO)

• MCP_DEBUG - Enable debug mode: true or false (default: false)

Chart Settings

• CHART_DEFAULT_WIDTH - Default chart width in pixels (default: 800)

• CHART_DEFAULT_HEIGHT - Default chart height in pixels (default: 600)

• CHART_DEFAULT_DPI - Default chart DPI (default: 100)

• CHART_MAX_DATA_POINTS - Maximum data points per chart (default: 10000)

Command Line Usage

With uvx (recommended):

uvx --from git+https://github.com/mr901/mcp-plots.git mcp-plots --help

# Examples:
uvx --from git+https://github.com/mr901/mcp-plots.git mcp-plots --port 8080 --log-level DEBUG
uvx --from git+https://github.com/mr901/mcp-plots.git mcp-plots --chart-width 1200 --chart-height 800

Traditional Python:

python -m src --help

# Examples:
python -m src --transport streamable-http --host 0.0.0.0 --port 8000
python -m src --log-level DEBUG --chart-width 1200 --chart-height 800

Docker

Build image:

docker build -t mcp-plots .

Run container with custom configuration:

docker run --rm -p 8000:8000 \
 -e MCP_TRANSPORT=streamable-http \
 -e MCP_HOST=0.0.0.0 \
 -e MCP_PORT=8000 \
 -e LOG_LEVEL=INFO \
 -e CHART_DEFAULT_WIDTH=1000 \
 -e CHART_DEFAULT_HEIGHT=700 \
 -e CHART_DEFAULT_DPI=150 \
 -e CHART_MAX_DATA_POINTS=5000 \
 mcp-plots

Cursor MCP Integration

Quick Setup for Cursor

The Plots MCP Server is designed to work seamlessly with Cursor's MCP support. Here's how to integrate it:

1. Add to Cursor's MCP Configuration

Add this to your Cursor MCP configuration file (~/.cursor/mcp.json or similar):

{
 "mcpServers": {
 "plots": {
 "command": "uvx",
 "args": [
 "--from", 
 "git+https://github.com/mr901/mcp-plots.git@main",
 "mcp-plots",
 "--transport", 
 "stdio"
 ],
 "env": {
 "LOG_LEVEL": "INFO",
 "CHART_DEFAULT_WIDTH": "800",
 "CHART_DEFAULT_HEIGHT": "600"
 }
 }
 }
}

2. Alternative: HTTP Transport

For HTTP-based integration:

{
 "mcpServers": {
 "plots-http": {
 "command": "uvx",
 "args": [
 "--from", 
 "git+https://github.com/mr901/mcp-plots.git@main", 
 "mcp-plots",
 "--transport", 
 "streamable-http",
 "--host", 
 "127.0.0.1",
 "--port", 
 "8000"
 ]
 }
 }
}

3. Local Development Setup

For local development (if you have the code cloned):

{
 "mcpServers": {
 "plots-dev": {
 "command": "python",
 "args": ["-m", "src", "--transport", "stdio"],
 "cwd": "/path/to/mcp-plots",
 "env": {
 "LOG_LEVEL": "DEBUG"
 }
 }
 }
}

4. Verify Integration

After adding the configuration:

1. Restart Cursor

2. Check MCP connection in Cursor's MCP panel

3. Test with a simple chart:

   Create a bar chart showing sales data: A=100, B=150, C=80

MERMAID-First Approach

This server prioritizes MERMAID output by default because:

• ✅ Renders instantly in Cursor - No external viewers needed

• ✅ Interactive - Cursor can analyze and discuss the diagrams

• ✅ Lightweight - Fast generation and display

• ✅ Scalable - Vector-based, works at any zoom level

Chart Types with Native MERMAID Support:

• line, bar, pie, area → xychart-beta format

• histogram → xychart-beta with automatic binning

• funnel → Styled flowchart with color gradients

• gauge → Flowchart with color-coded value indicators

• sankey → Flow diagrams with source/target styling

Available Tools

render_chart

Main chart generation tool with MERMAID-first approach.

Parameters:

• chart_type - Chart type (line, bar, pie, scatter, heatmap, etc.)

• data - List of data objects

• field_map - Field mappings (x_field, y_field, category_field, etc.)

• config_overrides - Chart configuration overrides

• output_format - Output format (mermaid [default], mcp_image, mcp_text)

Special Modes:

• chart_type="help" - Show available chart types and themes

• chart_type="suggest" - Analyze data and suggest field mappings

configure_preferences

Interactive configuration tool for setting user preferences.

Parameters:

• output_format - Default output format (mermaid, mcp_image, mcp_text)

• theme - Default theme (default, dark, seaborn, minimal)

• chart_width - Default chart width in pixels

• chart_height - Default chart height in pixels

• reset_to_defaults - Reset all preferences to system defaults

Features:

• Persistent Settings - Saved to ~/.plots_mcp_config.json

• Live Preview - Shows sample chart with current settings

• Override Support - Use config_overrides for one-off changes

Documentation

Additional Resources

• Complete Documentation - Technical documentation hub

• Quick Start - 5-minute setup guide

• Integration Guide - MCP client setup and configuration

• API Reference - Complete tool specifications and examples

• Advanced Guide - Architecture, deployment, and development

• Sample Prompts - Ready-to-use testing examples

Chart Examples

Basic Bar Chart:

{
 "chart_type": "bar",
 "data": [
 {"category": "Sales", "value": 120},
 {"category": "Marketing", "value": 80},
 {"category": "Support", "value": 60}
 ],
 "field_map": {
 "category_field": "category", 
 "value_field": "value"
 }
}

Time Series Line Chart:

{
 "chart_type": "line",
 "data": [
 {"date": "2024-01", "revenue": 1000},
 {"date": "2024-02", "revenue": 1200},
 {"date": "2024-03", "revenue": 1100}
 ],
 "field_map": {
 "x_field": "date",
 "y_field": "revenue"
 }
}

Funnel Chart:

{
 "chart_type": "funnel",
 "data": [
 {"stage": "Awareness", "value": 1000},
 {"stage": "Interest", "value": 500}, 
 {"stage": "Purchase", "value": 100}
 ],
 "field_map": {
 "category_field": "stage",
 "value_field": "value"
 }
}

🔧 Configuration

Environment Variables

• MCP_TRANSPORT - Transport type (streamable-http | stdio)

• MCP_HOST - Host address (default: 0.0.0.0)

• MCP_PORT - Port number (default: 8000)

• LOG_LEVEL - Logging level (default: INFO)

• MCP_DEBUG - Enable debug mode (true | false)

• CHART_DEFAULT_WIDTH - Default chart width in pixels (default: 800)

• CHART_DEFAULT_HEIGHT - Default chart height in pixels (default: 600)

• CHART_DEFAULT_DPI - Default chart DPI (default: 100)

• CHART_MAX_DATA_POINTS - Maximum data points per chart (default: 10000)

User Preferences

Personal preferences are stored in ~/.plots_mcp_config.json:

{
 "defaults": {
 "output_format": "mermaid",
 "theme": "default",
 "chart_width": 800,
 "chart_height": 600
 },
 "user_preferences": {
 "output_format": "mcp_image",
 "theme": "dark"
 }
}

🚀 Advanced Usage

Custom Themes

Available themes: default, dark, seaborn, minimal, whitegrid, darkgrid, ticks

High-Resolution Charts

uvx --from git+https://github.com/mr901/mcp-plots.git mcp-plots \
 --chart-width 1920 \
 --chart-height 1080 \
 --chart-dpi 300

Performance Optimization

• Use max_data_points to limit large datasets

• MERMAID output is fastest for quick visualization

• PNG output for high-quality static images

• SVG output for scalable vector graphics

🐛 Troubleshooting

Common Issues

Issue: Charts not rendering in Cursor

• Solution: Ensure output_format="mermaid" (default)

• Check: MCP server connection in Cursor

Issue: uvx command not found

• Solution: Install uv: curl -LsSf https://astral.sh/uv/install.sh | sh

Issue: Port already in use

• Solution: Use different port: --port 8001

Issue: Large datasets slow

• Solution: Sample data or increase --max-data-points

Debug Mode

uvx --from git+https://github.com/mr901/mcp-plots.git mcp-plots \
 --debug \
 --log-level DEBUG

📝 Notes

• Matplotlib runs headless (Agg backend) in the container

• For large datasets, sample your data for responsiveness

• Chart defaults can be overridden per-request via config_overrides

• MERMAID charts render instantly in Cursor for the best user experience

• User preferences persist across sessions and apply to all charts by default

Maintenance

Resources

• GitHub Repository
Need Help?Related Servers

Tools

• configure_preferencesA
• render_chartA