JoeSandboxMCP

Instructions

Implementation Reference

• jbxmcp/tools.py:254-325 (handler)

  The core handler function for the 'get_ai_summaries' tool. Decorated with @mcp.tool() for automatic registration. It fetches the analysis report XML (cached or downloads if needed), parses the LLM reasonings section, extracts text and attributes from each reasoning element, and returns a structured dictionary with the summaries.
  @mcp.tool() async def get_ai_summaries(webid: str, run: int=0) -> Dict[str, Any]: """ Retrieve the AI summaries for a specific analysis run, either from cache or by downloading it. Joe Sandbox analyses may run on multiple system configurations (e.g., different Windows/Linux variants). Each run is indexed in the `runs` array of the analysis metadata. This function retrieves the report corresponding to a specific run. Args: webid: The submission ID of the analysis (unique identifier). run (optional, default = 0): The index of the analysis run to retrieve the report for. Use 0 for the first run, 1 for the second, etc. If not specified, defaults to 0 (the first run). Returns: A dictionary containing AI reasoning summaries with fields: - webid: The analysis ID - run: The run index - reasonings: List of AI reasoning entries - count: Number of reasoning entries found Notes: - Reports are cached in memory by key: "{webid}-{run}". - Use `run` to distinguish between different environments used during analysis. """ try: root = await get_or_fetch_report(webid, run) if root is None: return {"error": f"Could not retrieve report for submission ID '{webid}', run {run}"} # Find all reasoning elements reasoning_elements = root.findall('./llm/reasonings/reasoning') if not reasoning_elements: return { "warning": "No AI reasoning summaries found in the report", "webid": webid, "run": run } # Extract the reasonings with their attributes reasonings = [] for i, reasoning in enumerate(reasoning_elements): # Find the text element within this reasoning text_element = reasoning.find('./text') if text_element is not None and text_element.text: reasoning_data = { "id": i + 1, "text": text_element.text } # Add any attributes from the reasoning element for key, value in reasoning.attrib.items(): reasoning_data[key] = value reasonings.append(reasoning_data) return { "webid": webid, "run": run, "reasonings": reasonings, "count": len(reasonings) } except Exception as e: return { "error": f"Failed to process AI summaries for submission ID '{webid}'. " f"Reason: {str(e)}" }
• jbxmcp/server.py:19-19 (registration)

  Import of tools.py in the MCP server script. Since tools are decorated with @mcp.tool(), this import triggers their automatic registration with the FastMCP instance.
  import jbxmcp.tools as tools
• jbxmcp/tools.py:2-17 (helper)

  The __all__ list in tools.py exports 'get_ai_summaries' among other tools, facilitating import and usage.
  __all__ = [ 'submit_analysis_job', 'search_analysis', 'get_analysis_info', 'get_ai_summaries', 'get_dropped_info', 'get_domain_info', 'get_ip_info', 'get_url_info', 'get_signature_info', 'get_unpacked_files', 'get_pcap_file', 'get_list_of_recent_analyses', 'get_process_info', 'get_memory_dumps' ]