Skip to main content
Glama
joesecurity

JoeSandboxMCP

Official
by joesecurity

get_dropped_info

Retrieve metadata and detection indicators for files dropped during a Joe Sandbox analysis to identify malicious activity.

Instructions

Retrieve metadata for files dropped in a completed analysis, along with their associated detection indicators.

This tool returns information about dropped files for a specific sandbox run of an analysis.
Each result includes relevant metadata and detection indicators where available.

Args:
    webid (required): The submission ID of the analysis.
    run (optional, default = 0): The index of the analysis run to inspect.
                                 Use 0 for the first run, 1 for the second, etc.
    only_malicious_elements (default: True): If True, returns only dropped files explicitly classified as malicious by the sandbox engine.
    only_malicious_indicators (default: True): If True, limits the returned indicators to those considered clearly malicious by the detection logic.
        This excludes low-impact behavioral signals and focuses on indicators with a high likelihood of malicious intent or confirmed threat classification.
        If False, all observed indicators are included regardless of their severity.

Returns:
    A dictionary with:
      - webid: The analysis ID.
      - malicious_dropped_files: A list of dropped files marked as malicious, each with:
          - filename
          - sha256
          - size
          - type
          - process (originating process)
          - dump_name (sandbox-internal reference)
          - category (e.g., "dropped", "modified")
          - indicators: List of triggered detection rules, if any. Each entry includes:
              - desc: Description of the matched detection rule.
              - data: Matched content or signature.
              - source: The detection subsystem responsible (e.g. Suricata, Sigma, global traffic etc.).
                  - impact: Either "high" or "low", indicating the severity or confidence of the detection.  
                      High-impact indicators are strongly associated with malicious behavior or confirmed threats.  
                      Low-impact indicators reflect general behavior or environmental traits that may not be malicious on their own.
      - count: Total number of malicious dropped files found
Notes:
    - Empty Array returned if no dropped file was gathered during the analysis

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
webidYes
runNo
only_malicious_elementsNo
only_malicious_indicatorsNo

Implementation Reference

  • The core handler function for the 'get_dropped_info' tool. It parses the XML report from Joe Sandbox to extract information about dropped files, including hashes, sizes, processes, and malicious indicators. Decorated with @mcp.tool() for registration.
    @mcp.tool()
    async def get_dropped_info(webid: str, run: int = 0, only_malicious_elements: bool=True, only_malicious_indicators: bool=True) -> Dict[str, Any]:
        """
        Retrieve metadata for files dropped in a completed analysis, along with their associated detection indicators.
    
        This tool returns information about dropped files for a specific sandbox run of an analysis.
        Each result includes relevant metadata and detection indicators where available.
    
        Args:
            webid (required): The submission ID of the analysis.
            run (optional, default = 0): The index of the analysis run to inspect.
                                         Use 0 for the first run, 1 for the second, etc.
            only_malicious_elements (default: True): If True, returns only dropped files explicitly classified as malicious by the sandbox engine.
            only_malicious_indicators (default: True): If True, limits the returned indicators to those considered clearly malicious by the detection logic.
                This excludes low-impact behavioral signals and focuses on indicators with a high likelihood of malicious intent or confirmed threat classification.
                If False, all observed indicators are included regardless of their severity.
    
        Returns:
            A dictionary with:
              - webid: The analysis ID.
              - malicious_dropped_files: A list of dropped files marked as malicious, each with:
                  - filename
                  - sha256
                  - size
                  - type
                  - process (originating process)
                  - dump_name (sandbox-internal reference)
                  - category (e.g., "dropped", "modified")
                  - indicators: List of triggered detection rules, if any. Each entry includes:
                      - desc: Description of the matched detection rule.
                      - data: Matched content or signature.
                      - source: The detection subsystem responsible (e.g. Suricata, Sigma, global traffic etc.).
                          - impact: Either "high" or "low", indicating the severity or confidence of the detection.  
                              High-impact indicators are strongly associated with malicious behavior or confirmed threats.  
                              Low-impact indicators reflect general behavior or environmental traits that may not be malicious on their own.
              - count: Total number of malicious dropped files found
        Notes:
            - Empty Array returned if no dropped file was gathered during the analysis
        """
    
        try:
            root = await get_or_fetch_report(webid, run)
            if root is None:
                return {"error": f"Could not retrieve or parse report for submission ID '{webid}'"}
    
            dropped_files = root.findall('./droppedinfo/hash')
            results = []
    
            for dropped in dropped_files:
                attrs = dropped.attrib
                if attrs.get("malicious", "").lower() == "true" or not only_malicious_elements:
                    indicators = get_indicators(dropped, only_malicious_indicators)
                    file_info = {
                        "filename": attrs.get("file"),
                        "sha256": attrs.get("value"),
                        "type": attrs.get("type"),
                        "size": attrs.get("size"),
                        "process": attrs.get("process"),
                        "dump_name": attrs.get("dump"),
                        "category": attrs.get("category"),
                        "indicators": indicators
                    }
                    for hash_entry in dropped.findall('./value'):
                        key = hash_entry.attrib.get('algo')
                        if key:
                            file_info[key] = hash_entry.text.lower()
    
                    # Drop any empty/null entries
                    file_info = {k: v for k, v in file_info.items() if v}
                    results.append(file_info)
    
            return {
                "webid": webid,
                "malicious_dropped_files": results,
                "count": len(results)
            }
    
        except Exception as e:
            return {
                "error": f"Failed to extract malicious dropped file data for submission ID '{webid}' run {run}. "
                         f"Reason: {str(e)}"
            }
  • jbxmcp/tools.py:2-17 (registration)
    The 'get_dropped_info' tool is listed in the module's __all__ export list, indicating it is publicly exposed as part of the tools module.
    __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'
    ]
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it specifies the return format in detail (dictionary structure), notes that empty arrays are returned if no dropped files were gathered, explains the filtering logic for malicious elements and indicators, and describes the impact levels of indicators. However, it doesn't mention potential errors, rate limits, or authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Args, Returns, Notes) and front-loaded purpose statement. While comprehensive, some sentences could be more concise (e.g., the repeated explanations of 'malicious' filtering). Overall, most content earns its place by adding necessary context beyond structured fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 parameters, 0% schema coverage, no annotations, and no output schema, the description provides exceptional completeness: it fully documents all parameters, explains the return structure in detail, includes important notes about empty returns, and gives operational context. No significant gaps remain given the complexity level.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema description coverage, the description fully compensates by providing comprehensive parameter semantics: it explains all 4 parameters (webid, run, only_malicious_elements, only_malicious_indicators) with clear definitions, default values, and operational implications. The detailed explanations of the boolean parameters' effects on filtering are particularly valuable beyond basic schema information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Retrieve metadata for files dropped in a completed analysis') and distinguishes it from sibling tools like 'get_dropped_files' by specifying it returns metadata and detection indicators rather than the files themselves. It explicitly mentions the resource ('dropped files') and context ('for a specific sandbox run of an analysis').

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by specifying it's for 'a completed analysis' and 'a specific sandbox run,' but doesn't explicitly state when to use this tool versus alternatives like 'get_dropped_files' or 'get_analysis_info.' It provides some operational guidance (e.g., 'Use 0 for the first run') but lacks explicit comparisons or exclusions for sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/joesecurity/joesandboxMCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server