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Professor MCP Server for analysis and visualization.

This server provides MCP tools for launching Professor visualization tools and performing data analysis tasks.

ProfessorServer

Bases: BaseMCPServer

MCP Server for Professor analysis and visualization.

Source code in src/mada_tools/surrogate/professor/server.py 
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class ProfessorServer(BaseMCPServer):
    """MCP Server for Professor analysis and visualization."""

    def __init__(self):
        super().__init__("Professor Analysis", "Professor analysis and visualization tools")

        # Initialize LLM client for image analysis
        try:
            self.llm_client = OpenAI(
                base_url=self.get_env_var("API_BASE_URL", "https://livai-api.llnl.gov"),
                api_key=self.get_env_var("API_KEY", required=True),
            )
            # Get model from environment or use default
            self.model = self.get_env_var("MODEL", "gpt-4o")
        except Exception:
            # LLM client is optional for some operations
            self.llm_client = None
            self.model = None

    def _register_tools(self):
        """Register MCP tools for Professor operations."""

        @self.mcp.tool()
        def launch_professor_gui(yaml_file: str) -> str:
            """
            Launch Professor GUI from the given YAML config.

            Args:
                yaml_file: Path to Professor YAML config file

            Returns:
                str: Confirmation message once GUI process has started
            """
            try:
                prof_vis_path = self.get_env_var("PROF_VIS_PATH", "/usr/workspace/prof/bin/prof-vis")
                cmd = [prof_vis_path, yaml_file]

                subprocess.Popen(
                    cmd,
                    stdout=subprocess.DEVNULL,
                    stderr=subprocess.DEVNULL,
                    start_new_session=True,
                )
                return "Professor GUI launched successfully"
            except Exception as e:
                raise ToolExecutionError(f"Failed to launch Professor GUI: {e}")

        @self.mcp.tool()
        def analyze_image_with_llm(image_path: str, prompt: str) -> str:
            """
            Use an LLM to analyze and describe an image based on a prompt.

            Args:
                image_path: Local file path to the image
                prompt: Question or instruction about the image

            Returns:
                str: LLM-generated description or error message
            """
            if not self.llm_client:
                return "LLM client not configured. Please set API_KEY environment variable."

            if not os.path.isfile(image_path):
                return f"Error: File not found: {image_path}"

            try:
                # Read and encode the image
                with open(image_path, "rb") as f:
                    img_bytes = f.read()
                img_b64 = base64.b64encode(img_bytes).decode()

                # Compose messages with dynamic prompt
                messages = [
                    {"role": "user", "content": prompt},
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "image_url",
                                "image_url": {"url": f"data:image/png;base64,{img_b64}"},
                            }
                        ],
                    },
                ]

                response = self.llm_client.chat.completions.create(messages=messages, model=self.model)
                return response.choices[0].message.content

            except Exception as e:
                raise ToolExecutionError(f"Failed to analyze image: {e}")

main()

Main entry point for the Professor MCP server.

Source code in src/mada_tools/surrogate/professor/server.py 
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def main():
    """Main entry point for the Professor MCP server."""
    server = ProfessorServer()
    server.run_with_args("professor")