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intruder-mcp

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list_scans

Retrieve and filter security scans from Intruder to monitor assessment progress, identify new service exposures, and track cloud configuration checks.

Instructions

    List scans in the Intruder account with optional filters.

    Args:
        status: Filter by scan status (in_progress, completed, cancelled, cancelled_no_active_targets,
               cancelled_no_valid_targets, analysing_results)
        scan_type: Filter by scan type (assessment_schedule, new_service, cloudbot_new_target,
                 rapid_remediation, advisory, cloud_security)
    
    The scan_type parameters mean:
        - assessment_schedule: Scans that run on a regular schedule
        - new_service: Scans that are triggered when a new service is exposed on a target
        - cloudbot_new_target: Scans that are triggered when CloudBot discovers a new target in a connected cloud account
        - rapid_remediation: Scans that a user can trigger to test if a specific issue has been remediated
        - advisory: An issue created by the Intruder security team based on their manual work
        - cloud_security: Scans of cloud accounts, checking the configuration of the resources in the cloud account
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNo
scan_typeNo

Implementation Reference

  • The main handler function for the 'list_scans' MCP tool. It accepts optional status and scan_type parameters, calls the API helper to fetch all matching scans, formats them into a readable string list, and returns it. The docstring provides detailed schema information for inputs.
    @mcp.tool()
    async def list_scans(status: Optional[str] = None, scan_type: Optional[str] = None) -> str:
        """
        List scans in the Intruder account with optional filters.
    
        Args:
            status: Filter by scan status (in_progress, completed, cancelled, cancelled_no_active_targets,
                   cancelled_no_valid_targets, analysing_results)
            scan_type: Filter by scan type (assessment_schedule, new_service, cloudbot_new_target,
                     rapid_remediation, advisory, cloud_security)
        
        The scan_type parameters mean:
            - assessment_schedule: Scans that run on a regular schedule
            - new_service: Scans that are triggered when a new service is exposed on a target
            - cloudbot_new_target: Scans that are triggered when CloudBot discovers a new target in a connected cloud account
            - rapid_remediation: Scans that a user can trigger to test if a specific issue has been remediated
            - advisory: An issue created by the Intruder security team based on their manual work
            - cloud_security: Scans of cloud accounts, checking the configuration of the resources in the cloud account
        """
        scans = api.list_scans_all(status=status, scan_type=scan_type)
        formatted = [f"{scan.id} - {scan.scan_type} ({scan.status})" for scan in scans]
        return "\n".join(formatted)
  • Supporting helper method in IntruderAPI class that paginates through all scans using the API, yielding ScanList objects filtered by scan_type, schedule_period, or status. Called by the tool handler.
    def list_scans_all(self, scan_type: Optional[str] = None, schedule_period: Optional[str] = None,
                      status: Optional[str] = None) -> Generator[ScanList, None, None]:
        offset = 0
        while True:
            response = self.list_scans(scan_type=scan_type, schedule_period=schedule_period,
                                     status=status, limit=100, offset=offset)
            for scan in response.results:
                yield scan
            if not response.next:
                break
            offset += len(response.results)
  • Pydantic enum defining valid scan_type values for input validation in list_scans tool and API calls.
    class ScanTypeEnum(str, Enum):
        ASSESSMENT_SCHEDULE = "assessment_schedule"
        NEW_SERVICE = "new_service"
        CLOUDBOT_NEW_TARGET = "cloudbot_new_target"
        RAPID_REMEDIATION = "rapid_remediation"
        ADVISORY = "advisory"
        CLOUD_SECURITY = "cloud_security"
  • Pydantic enum defining valid status values for input validation in list_scans tool and API calls.
    class ScanStatusField(str, Enum):
        IN_PROGRESS = "in_progress"
        COMPLETED = "completed"
        CANCELLED = "cancelled"
        CANCELLED_NO_ACTIVE_TARGETS = "cancelled_no_active_targets"
        CANCELLED_NO_VALID_TARGETS = "cancelled_no_valid_targets"
        ANALYSING_RESULTS = "analysing_results"
  • The @mcp.tool() decorator registers the list_scans function as an MCP tool.
    @mcp.tool()
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states it's a list operation with filtering, implying read-only behavior, but doesn't mention pagination, rate limits, authentication requirements, or what happens if no filters are applied. For a list tool with zero annotation coverage, this leaves significant behavioral gaps.

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 and appropriately sized. It starts with a clear purpose statement, then documents parameters with detailed explanations. The bullet-point list for scan_type values is organized and easy to parse. Some minor verbosity exists in the scan_type explanations, but overall it's efficient and front-loaded.

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

Completeness3/5

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

Given 2 parameters with 0% schema coverage and no annotations or output schema, the description does well on parameter documentation but lacks other context. It doesn't describe return values (format, structure), error conditions, or behavioral constraints like pagination. For a list tool with filtering, this is adequate but has clear gaps in output and usage context.

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?

The description adds substantial value beyond the input schema. The schema has 0% description coverage and only provides parameter names and types. The description explains both parameters ('status' and 'scan_type'), lists all possible values for 'status', and provides detailed explanations for each 'scan_type' value. This fully compensates for the schema's lack of documentation.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'List scans in the Intruder account with optional filters.' It specifies the verb ('List'), resource ('scans'), and scope ('Intruder account'), but doesn't explicitly differentiate from sibling tools like 'get_scan' (singular) or 'list_issues' (different resource). The purpose is clear but lacks sibling comparison.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It mentions optional filters but doesn't explain scenarios where filtering is needed or when to choose this over other list tools (e.g., 'list_targets'). No explicit when/when-not statements or alternative tool references are included.

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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