vizro-mcp

Instructions

Implementation Reference

• vizro-mcp/src/vizro_mcp/server.py:324-370 (handler)

  The core handler function for the 'validate_chart_code' tool, registered via @mcp.tool() decorator. Validates ChartPlan input, generates executable Python code for the chart and a dashboard template, creates pycafe preview URL if data is remote, and optionally opens it in browser.
  @mcp.tool() def validate_chart_code( chart_config: ChartPlan = Field(description="A ChartPlan object with the chart configuration"), data_info: DFMetaData = Field(description="Metadata for the dataset to be used in the chart"), auto_open: bool = Field(default=True, description="Whether to automatically open the PyCafe link in a browser"), ) -> ValidateResults: """Validate the chart code created by the user and optionally open the PyCafe link in a browser. Returns: ValidationResults object with status and dashboard details """ Vizro._reset() try: chart_plan_obj = ChartPlan.model_validate(chart_config) except ValidationError as e: return ValidateResults( valid=False, message=f"Validation Error: {e!s}", python_code="", pycafe_url=None, browser_opened=False, ) else: dashboard_code = chart_plan_obj.get_dashboard_template(data_info=data_info) # Generate PyCafe URL if all data is remote pycafe_url = create_pycafe_url(dashboard_code) if data_info.file_location_type == "remote" else None browser_opened = False if auto_open and pycafe_url: try: browser_opened = webbrowser.open(pycafe_url) except Exception: browser_opened = False return ValidateResults( valid=True, message="Chart only dashboard created successfully!", python_code=chart_plan_obj.get_chart_code(vizro=True), pycafe_url=pycafe_url, browser_opened=browser_opened, ) finally: Vizro._reset()
• vizro-mcp/src/vizro_mcp/server.py:44-53 (schema)

  Dataclass defining the output schema returned by the validate_chart_code tool (and other validation tools), including validation status, message, generated Python code, pycafe URL, and browser open status.
  @dataclass class ValidateResults: """Results of validation tools.""" valid: bool message: str python_code: str pycafe_url: str | None browser_opened: bool
• vizro-mcp/src/vizro_mcp/_schemas/schemas.py:106-217 (schema)

  Pydantic BaseModel ChartPlan defining the primary input schema for the validate_chart_code tool. Includes fields for chart_type, chart_name, imports, and chart_code with custom validator. Provides helper methods get_imports, get_chart_code, and get_dashboard_template used directly in the handler.
  class ChartPlan(BaseModel): """Base chart plan used to generate chart code based on user visualization requirements.""" chart_type: str = Field( description=""" Describes the chart type that best reflects the user request. """, ) chart_name: str = Field( description=""" The name of the chart function. Should be unique, concise and in snake_case. """, pattern=r"^[a-z][a-z0-9_]*$", ) imports: list[str] = Field( description=""" List of import statements required to render the chart defined by the `chart_code` field. Ensure that every import statement is a separate list/array entry: An example of valid list of import statements would be: ["import pandas as pd", "import plotly.express as px"] """, ) chart_code: Annotated[ str, AfterValidator(_check_chart_code), Field( description=""" Python code that generates a generates a plotly go.Figure object. It must fulfill the following criteria: 1. Must be wrapped in a function that is named `chart_name` 2. Must accept as first argument argument `data_frame` which is a pandas DataFrame 3. Must return a plotly go.Figure object 4. All data used in the chart must be derived from the data_frame argument, all data manipulations must be done within the function. 5. DO NOT modify the background (with plot_bgcolor) or color sequences unless explicitly asked for 6. When creating hover templates, explicitly ensure that it works on light and dark mode """, ), ] _base_chart_imports: list[str] = PrivateAttr(BASE_IMPORTS) def get_imports(self, vizro: bool = False): imports = list(dict.fromkeys(self.imports + self._base_chart_imports)) # remove duplicates if vizro: imports = [imp for imp in imports if "import plotly.express as px" not in imp] else: imports = [imp for imp in imports if "vizro" not in imp] return "\n".join(imports) + "\n" def get_chart_code(self, chart_name: str | None = None, vizro: bool = False): chart_code = self.chart_code if vizro: chart_code = chart_code.replace(f"def {self.chart_name}", f"@capture('graph')\ndef {self.chart_name}") if chart_name is not None: chart_code = chart_code.replace(f"def {self.chart_name}", f"def {chart_name}") return chart_code def get_dashboard_template(self, data_info: DFMetaData) -> str: """Create a simple dashboard template for displaying the chart. Args: data_info: The metadata of the dataset to use. Returns: Complete Python code for a Vizro dashboard displaying the chart. """ chart_code = self.get_chart_code(vizro=True) imports = self.get_imports(vizro=True) # Add the Vizro-specific imports if not present additional_dashboard_imports = [ "import vizro.models as vm", "from vizro import Vizro", "from vizro.managers import data_manager", ] # Combine imports without duplicates all_imports = list(dict.fromkeys(additional_dashboard_imports + imports.split("\n"))) dashboard_template = f""" {chr(10).join(imp for imp in all_imports if imp)} # Load the data data_manager["{data_info.file_name}"] = {data_info.read_function_string}("{data_info.file_path_or_url}") # Custom chart code {chart_code} # Create a dashboard to display the chart dashboard = vm.Dashboard( pages=[ vm.Page( title="{self.chart_type.capitalize()} Chart", components=[ vm.Graph( id="{self.chart_type}_graph", figure={self.chart_name}("{data_info.file_name}"), ) ], ) ], title="{self.chart_type.capitalize()} Dashboard", ) # Run the dashboard Vizro().build(dashboard).run() """ return dashboard_template