Fermat MCP

Instructions

Implementation Reference

• fmcp/mpl_mcp/core/stack_chart.py:8-152 (handler)

  The handler function plot_stack that implements the stacked area/bar chart plotting logic using matplotlib, handling inputs, creating the plot, and returning a PNG Image.
  def plot_stack( x_data: Union[List[Union[float, int]], List[List[Union[float, int]]]], y_data: Union[List[List[Union[float, int]]], List[Union[float, int]]], chart_type: str = "area", # "area" or "bar" labels: Optional[List[str]] = None, title: str = "", xlabel: str = "", ylabel: str = "", colors: Optional[Union[str, List[str]]] = None, alpha: float = 0.7, dpi: int = 200, figsize: Optional[List[Union[int, float]]] = None, grid: bool = True, legend: bool = True, ) -> Image: """ Create a stacked area or bar chart. Args: x_data: X-axis data points (1D array-like) y_data: 2D array-like where each row represents a series to be stacked chart_type: Type of chart to create ("area" or "bar") labels: List of labels for the data series title: Plot title xlabel: Label for the x-axis ylabel: Label for the y-axis colors: Color or list of colors for the areas/bars alpha: Alpha transparency (0-1, default: 0.7) 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: True) Returns: FastMCP Image object with the plotted stack chart """ # Convert inputs to numpy arrays with explicit float type x = np.asarray(x_data, dtype=float) y = np.asarray(y_data, dtype=float) # Ensure y_data is 2D if y.ndim == 1: y = y.reshape(1, -1) # Handle labels if labels is None: labels_list = [f"Series {i+1}" for i in range(y.shape[0])] elif isinstance(labels, str): labels_list = [labels] else: labels_list = list(labels) # Handle colors if colors is None: # Default color cycle default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] colors = [default_colors[i % len(default_colors)] for i in range(y.shape[0])] elif isinstance(colors, str): colors = [str(colors)] * y.shape[0] elif isinstance(colors, list): # If the list is empty or contains None, use default colors if not colors or all(c is None for c in colors): default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] colors = [default_colors[i % len(default_colors)] for i in range(y.shape[0])] else: # Filter out None values and ensure all elements are strings default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] colors = [str(c) if c is not None else default_colors[i % len(default_colors)] for i, c in enumerate(colors)] # If colors list is shorter than y.shape[0], repeat colors if len(colors) < y.shape[0]: colors += [default_colors[i % len(default_colors)] for i in range(len(colors), y.shape[0])] else: colors = [str(colors)] * y.shape[0] # Ensure colors is always a list for safe usage of len(colors) if not isinstance(colors, list): colors = [str(colors)] * y.shape[0] # If colors is still None (edge case), set to default colors if colors is None: default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] colors = [default_colors[i % len(default_colors)] for i in range(y.shape[0])] # Create figure with specified size using OO interface # Normalize figsize if figsize and len(figsize) >= 2: figsize_vals = (float(figsize[0]), float(figsize[1])) else: figsize_vals = (8.0, 6.0) fig, ax = plt.subplots(figsize=figsize_vals, dpi=dpi) # Calculate the cumulative sum for stacking y_stack = np.cumsum(y, axis=0) # Create the stacked chart for i in range(y.shape[0] - 1, -1, -1): if i == 0: y_plot = y_stack[i] y_bottom = np.zeros_like(x) else: y_plot = y_stack[i] - y_stack[i - 1] y_bottom = y_stack[i - 1] if chart_type == "area": ax.fill_between( x, y_bottom, y_stack[i], label=labels_list[i] if i < len(labels_list) else f"Series {i+1}", color=colors[i % len(colors)], alpha=alpha, edgecolor="none", ) elif chart_type == "bar": ax.bar( x, y_plot, bottom=y_bottom, label=labels_list[i] if i < len(labels_list) else f"Series {i+1}", color=colors[i % len(colors)], alpha=alpha, edgecolor="none", width=0.8 if len(x) > 1 else 0.8 * (x[1] - x[0]) if len(x) > 1 else 0.8, ) # Customize the plot ax.set_title(title) ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) if grid: ax.grid(True, linestyle="--", alpha=0.7) if legend: ax.legend() plt.tight_layout() # Save the plot to a buffer and close the figure 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:23-23 (registration)

  Registration of the plot_stack tool in the mpl_mcp FastMCP server instance, which likely names it 'mpl_mcp_plot_stack'.
  mpl_mcp.tool(plot_stack, description="Plots stacked area/bar chart of given datavalues")
• fmcp/mpl_mcp/core/stack_chart.py:8-22 (schema)

  Type hints and docstring in the plot_stack function define the input schema and output (Image).
  def plot_stack( x_data: Union[List[Union[float, int]], List[List[Union[float, int]]]], y_data: Union[List[List[Union[float, int]]], List[Union[float, int]]], chart_type: str = "area", # "area" or "bar" labels: Optional[List[str]] = None, title: str = "", xlabel: str = "", ylabel: str = "", colors: Optional[Union[str, List[str]]] = None, alpha: float = 0.7, dpi: int = 200, figsize: Optional[List[Union[int, float]]] = None, grid: bool = True, legend: bool = True, ) -> Image: