Fermat MCP

Instructions

Implementation Reference

• fmcp/mpl_mcp/core/scatter_chart.py:8-130 (handler)

  The handler function plot_scatter that implements the core logic for creating a customizable scatter plot using matplotlib.pyplot, processes input data into numpy arrays, handles multiple series, customizes appearance, and returns a PNG Image object.

  def plot_scatter(
      x_data: List[Union[float, int]],
      y_data: List[Union[float, int]],
      labels: Optional[Union[str, List[str]]] = None,
      title: str = "",
      xlabel: str = "",
      ylabel: str = "",
      color: Union[str, List[str]] = "blue",
      size: Union[int, float, List[float]] = 36,
      alpha: float = 0.7,
      marker: str = "o",
      edgecolors: Optional[str] = "face",
      linewidths: float = 1.0,
      save: bool = False,
      dpi: int = 200,
      figsize: Optional[List[Union[int, float]]] = None,
      grid: bool = True,
      legend: bool = False,
  ) -> Image:
      """
      Create a scatter plot with customizable appearance.
  
      Args:
          x_data: X-axis data points (1D or 2D sequence for multiple series)
          y_data: Y-axis data points (1D or 2D sequence for multiple series)
          labels: Label or list of labels for the data series
          title: Plot title
          xlabel: Label for the x-axis
          ylabel: Label for the y-axis
          color: Color or list of colors for the markers (default: "blue")
          size: Size or list of sizes for the markers (default: 36)
          alpha: Alpha transparency (0-1, default: 0.7)
          marker: Marker style (default: "o" for circle)
          edgecolors: Color of marker edges (default: "face")
          linewidths: Width of marker edges (default: 1.0)
          save: If True, save the figure to a buffer
          dpi: Output image resolution (dots per inch, default: 200)
          figsize: Figure size as (width, height) in inches
          grid: Whether to show grid lines (default: True)
          legend: Whether to show legend (default: False)
  
      Returns:
          FastMCP Image object with the plotted scatter chart
      """
      # Convert inputs to numpy arrays for processing
      x = np.asarray(x_data, dtype=float)
      y = np.asarray(y_data, dtype=float)
  
      # Ensure y is at least 2D
      if y.ndim == 1:
          y = y.reshape(-1, 1)
  
      # Ensure x is 1D and matches the number of data points in y
      x = np.asarray(x_data, dtype=float).flatten()
      if len(x) != y.shape[0]:
          x = np.tile(x, (y.shape[1], 1)).T.flatten()
          y = y.T.reshape(-1, y.shape[0]).T
  
      # Handle labels — normalize to a list
      if labels is None:
          labels_list: List[str] = [""] * y.shape[1]
      elif isinstance(labels, str):
          labels_list = [labels]
      else:
          labels_list = list(labels)
  
      # Handle colors — normalize to a list
      if isinstance(color, str):
          colors_list = [color] * y.shape[1]
      else:
          colors_list = list(color)
  
      # Handle sizes — normalize to list of floats
      if isinstance(size, (int, float)):
          sizes_list = [float(size)] * y.shape[1]
      elif isinstance(size, (list, tuple, np.ndarray)):
          sizes_list = [float(s) for s in list(size)]
      else:
          sizes_list = [36.0] * y.shape[1]
  
      # Normalize figsize and create figure
      if figsize and len(figsize) >= 2:
          figsize_vals = (float(figsize[0]), float(figsize[1]))
      else:
          figsize_vals = (6.0, 4.0)
  
      fig, ax = plt.subplots(figsize=figsize_vals, dpi=dpi)
  
      # Create the scatter plot for each series
      for i in range(y.shape[1]):
          current_label = labels_list[i] if i < len(labels_list) else f"Series {i+1}"
          current_color = colors_list[i % len(colors_list)]
          current_size = sizes_list[i % len(sizes_list)]
  
          ax.scatter(
              x,
              y[:, i],
              label=current_label,
              c=current_color,
              s=current_size,
              alpha=alpha,
              marker=marker,
              edgecolors=edgecolors,
              linewidths=linewidths,
          )
  
      # Customize the plot
      ax.set_title(title)
      ax.set_xlabel(xlabel)
      ax.set_ylabel(ylabel)
  
      if grid:
          ax.grid(True)
  
      if legend and any(labels_list):
          ax.legend()
  
      # Save to buffer and return
      buf = io.BytesIO()
      fig.savefig(buf, format="png", dpi=dpi, bbox_inches="tight")
      plt.close(fig)
      buf.seek(0)
      return Image(data=buf.read(), format="png")

• fmcp/mpl_mcp/server.py:20-20 (registration)

  Registers the plot_scatter function as an MCP tool on the mpl_mcp FastMCP server instance with a description.

  mpl_mcp.tool(plot_scatter, description="Plots scatter chart of given datavalues")

• fmcp/mpl_mcp/server.py:3-3 (registration)

  Imports the plot_scatter handler from core.scatter_chart for registration.

  from .core.scatter_chart import plot_scatter