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intruder-io

intruder-mcp

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get_scanner_output

Retrieve scanner output for a specific issue occurrence to analyze security findings and identify vulnerabilities in Intruder.IO scans.

Instructions

    Get scanner output for a specific occurrence of an issue.

    Args:
        issue_id: The ID of the issue
        occurrence_id: The ID of the occurrence
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
issue_idYes
occurrence_idYes

Implementation Reference

  • MCP tool handler for 'get_scanner_output'. Decorated with @mcp.tool(), it calls the API client to fetch all scanner outputs for the given issue and occurrence, formats them with plugin info and output lines, and returns as a string. This is both the handler logic and registration point.
    @mcp.tool()
    async def get_scanner_output(issue_id: int, occurrence_id: int) -> str:
        """
        Get scanner output for a specific occurrence of an issue.
    
        Args:
            issue_id: The ID of the issue
            occurrence_id: The ID of the occurrence
        """
        outputs = api.get_scanner_output_all(issue_id=issue_id, occurrence_id=occurrence_id)
        formatted = []
        for output in outputs:
            plugin_info = f"Plugin: {output.plugin.name}"
            if output.plugin.cve:
                plugin_info += f" (CVEs: {', '.join(output.plugin.cve)})"
            formatted.append(plugin_info)
            formatted.append("Output:")
            formatted.extend(str(line) for line in output.scanner_output)
            formatted.append("")
        return "\n".join(formatted)
  • Helper method in IntruderAPI class that fetches all scanner outputs by paginating through the API endpoints using get_scanner_output.
    def get_scanner_output_all(self, issue_id: int, occurrence_id: int) -> Generator[ScannerOutputList, None, None]:
        offset = 0
        while True:
            response = self.get_scanner_output(issue_id, occurrence_id, limit=100, offset=offset)
            for output in response.results:
                yield output
            if not response.next:
                break
            offset += len(response.results)
  • Core API client method that queries the Intruder API for paginated scanner output data for a specific issue occurrence.
    def get_scanner_output(self, issue_id: int, occurrence_id: int,
                          limit: Optional[int] = None, offset: Optional[int] = None) -> PaginatedScannerOutputListList:
        params = {}
        if limit:
            params["limit"] = limit
        if offset:
            params["offset"] = offset
        return PaginatedScannerOutputListList(**self.client.get(f"{self.base_url}/issues/{issue_id}/occurrences/{occurrence_id}/scanner_output/", params=params).json())
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves output but does not describe what the output contains (e.g., format, content), whether it requires authentication, rate limits, or error conditions. This is a significant gap for a tool with no annotation coverage.

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 appropriately sized and front-loaded, with the purpose stated clearly in the first sentence and parameters listed efficiently. There is no wasted text, though the structure could be slightly improved by integrating the Args section more seamlessly.

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

Completeness2/5

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

Given the complexity (a retrieval tool with two parameters), no annotations, and no output schema, the description is incomplete. It lacks details on output format, error handling, and behavioral traits, making it inadequate for the agent to fully understand how to use the tool effectively.

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

Parameters3/5

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

The schema description coverage is 0%, but the description compensates by listing and naming both parameters ('issue_id' and 'occurrence_id') in the Args section, providing basic semantics. However, it does not explain what these IDs represent, their format, or how to obtain them, leaving gaps in understanding.

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 with a specific verb ('Get') and resource ('scanner output'), and specifies the scope ('for a specific occurrence of an issue'). However, it does not explicitly distinguish this tool from sibling tools like 'list_occurrences' or 'get_scan', which might provide related but different functionality.

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 does not mention prerequisites, such as needing an existing issue and occurrence, or compare it to sibling tools like 'list_occurrences' or 'get_scan', leaving the agent to infer usage context.

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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