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