Plots MCP Server

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

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):
   config.json

   Copy

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

   Alternative (zero-install via uvx + PyPI):
   config.json

   Copy

   {
     "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