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joesecurity

JoeSandboxMCP

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by joesecurity

get_url_info

Extract URLs and detection indicators from Joe Sandbox analysis to identify malicious web activity with severity filtering options.

Instructions

Retrieve urls in a completed analysis, along with their associated detection indicators.

This tool extracts urls gathered by the sandbox engine and returns relevant context such as ip address, source, and detection metadata.
Optional filtering parameters allow control over the inclusion of urls and indicators based on their assessed severity.

Args:
    webid (required): The submission ID of the analysis.
    run (default: 0): Index of the sandbox run to inspect (from the `runs` array in analysis info).
    only_malicious_elements (default: True): If True, returns only urls 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 containing a list of malicious URLs. Each entry includes:
      - url: The observed URL (may be truncated if extremely long).
      - ip: The resolved IP address associated with the URL (if available).
      - fromMemory: Whether the URL was extracted from memory.
      - source: Subsystem or extraction context (e.g., browser, process).
      - malicious: 'true' for urls classified as malicious
      - 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.

Notes:
    - Very long URLs are truncated for readability but include their original length as a hint.
    - Empty Array returned if no url 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_url_info' tool, decorated with @mcp.tool(). It parses the Joe Sandbox XML report to extract URL indicators of compromise (IOCs), filters by maliciousness, handles long URL truncation, attaches detection indicators using get_indicators helper, and returns a list of URL details.
    @mcp.tool()
    async def get_url_info(webid: str, run: int = 0, only_malicious_elements: bool=True, only_malicious_indicators: bool=True) -> Dict[str, Any]:
        """
        Retrieve urls in a completed analysis, along with their associated detection indicators.
    
        This tool extracts urls gathered by the sandbox engine and returns relevant context such as ip address, source, and detection metadata.
        Optional filtering parameters allow control over the inclusion of urls and indicators based on their assessed severity.
    
        Args:
            webid (required): The submission ID of the analysis.
            run (default: 0): Index of the sandbox run to inspect (from the `runs` array in analysis info).
            only_malicious_elements (default: True): If True, returns only urls 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 containing a list of malicious URLs. Each entry includes:
              - url: The observed URL (may be truncated if extremely long).
              - ip: The resolved IP address associated with the URL (if available).
              - fromMemory: Whether the URL was extracted from memory.
              - source: Subsystem or extraction context (e.g., browser, process).
              - malicious: 'true' for urls classified as malicious
              - 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.
    
        Notes:
            - Very long URLs are truncated for readability but include their original length as a hint.
            - Empty Array returned if no url 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}' run {run}"}
            urlinfo = root.findall("./urlinfo/url")
            urls = []
            for url_entry in urlinfo:
                attrs = url_entry.attrib
                if attrs.get("malicious") == "true" or not only_malicious_elements:
                    indicators = get_indicators(url_entry, only_malicious_indicators)
                    url = attrs.get("name")
                    orig_len = len(url)
                    if len(url) > 150:
                        url = url[:150] + f" <truncated url, orig length={orig_len}>"
                    url = {
                        "url": url,
                        "ip": attrs.get("ip"),
                        "fromMemory": attrs.get("fromMemory"),
                        "source": attrs.get("source"),
                        "malicious": attrs.get("malicious"),
                        "indicators": indicators
                    }
                    urls.append(url)
            return urls
        except Exception as e:
            return {
                "error": f"Failed to get URL IOCs for submission ID '{webid}' run {run}. "
                         f"Reason: {str(e)}"
            }
  • jbxmcp/tools.py:2-17 (registration)
    The 'get_url_info' tool is exported via the __all__ list, making it available for import from the 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 an excellent job disclosing behavioral traits. It explains what data gets returned, truncation behavior for long URLs, empty array returns when no URLs exist, and the filtering logic based on severity assessment. The only minor gap is lack of explicit mention about authentication requirements or rate limits.

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-loads the core purpose. While comprehensive, some sentences could be more concise (e.g., the explanation of 'only_malicious_indicators' uses multiple sentences that could be streamlined). Overall, most content earns its place.

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?

Given the tool's complexity (4 parameters, no annotations, no output schema), the description provides complete context. It explains the purpose, parameters, return structure with detailed field descriptions, and behavioral notes. The return value documentation effectively substitutes for a missing output schema by detailing the dictionary structure and all nested fields.

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?

With 0% schema description coverage, the description fully compensates by providing detailed semantic explanations for all 4 parameters. It clarifies that 'webid' is a required submission ID, 'run' is an index from the runs array, and both boolean parameters control filtering based on malicious classification with clear explanations of what 'True' and 'False' values mean.

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', 'extracts') and resources ('urls in a completed analysis', 'associated detection indicators'). It distinguishes from siblings by focusing specifically on URL extraction from sandbox analysis, unlike tools like get_domain_info or get_ip_info that handle different data types.

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

Usage Guidelines4/5

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

The description provides clear context about when to use this tool - specifically for retrieving URL information from completed sandbox analyses. However, it doesn't explicitly state when NOT to use it or name alternative tools for related but different purposes (e.g., using get_analysis_info for general analysis metadata).

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

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