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intruder-io

intruder-mcp

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create_targets

Add target addresses to Intruder.IO for security testing by specifying one or multiple addresses to create targets.

Instructions

    Create one or more targets.

    Args:
        addresses: List of target addresses to create
                Example: ['example.com'] for a single target
                Example: ['example.com', 'test.com'] for multiple targets
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressesYes

Implementation Reference

  • The main handler function for the 'create_targets' MCP tool. It accepts a list of addresses, converts them to target dicts, calls the API client's bulk_create_targets method, and returns a confirmation message.
    @mcp.tool()
    async def create_targets(addresses: List[str]) -> str:
        """
        Create one or more targets.
    
        Args:
            addresses: List of target addresses to create
                    Example: ['example.com'] for a single target
                    Example: ['example.com', 'test.com'] for multiple targets
        """
        targets = [{'address': addr} for addr in addresses]
        result = api.bulk_create_targets(targets)
        return f"Created {len(addresses)} targets"
  • The @mcp.tool() decorator registers the create_targets function as an MCP tool.
    @mcp.tool()
  • Helper method in IntruderAPI class that handles bulk creation of targets, avoiding duplicates by checking existing targets and making API call only for new ones.
    def bulk_create_targets(self, targets: List[Dict[str, str]]) -> List[Target]:
        # Get list of existing target addresses
        existing_targets = list(self.list_targets_all())
        existing_addresses = {target.address for target in existing_targets}
        
        # Filter out targets that already exist
        new_targets = [target for target in targets if target['address'] not in existing_addresses]
        
        if not new_targets:
            return [target for target in existing_targets if target.address in {t['address'] for t in targets}]
            
        response = self.client.post(f"{self.base_url}/targets/bulk/", json=new_targets)
        created_targets = [Target(**target) for target in response.json()]
        
        # Combine newly created targets with existing ones
        return created_targets + [target for target in existing_targets if target.address in {t['address'] for t in targets}]
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool creates targets, implying a mutation, but doesn't cover critical aspects like required permissions, whether the operation is idempotent, rate limits, or what happens on failure. This leaves significant gaps in understanding the tool's behavior beyond the basic action.

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 with two sentences and a structured 'Args' section, making it easy to parse. It front-loads the main purpose and efficiently explains the parameter with examples, though the formatting could be slightly cleaner (e.g., less indentation). Overall, it's concise with minimal waste.

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 of a mutation tool with no annotations and no output schema, the description is incomplete. It covers the basic action and parameter but lacks information on permissions, error handling, return values, or how it interacts with sibling tools. This leaves the agent with insufficient context for reliable use in a broader workflow.

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

Parameters4/5

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

The schema description coverage is 0%, so the description must compensate. It adds meaningful context by explaining that 'addresses' is a list of target addresses and provides clear examples for single and multiple targets. This goes beyond the bare schema, making the parameter's purpose and format understandable, though it doesn't detail constraints like address format or length limits.

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 action ('Create') and resource ('one or more targets'), making the purpose understandable. However, it doesn't differentiate this tool from sibling tools like 'create_target_tag' or 'create_scan', which also create related resources, so it doesn't reach the highest score for sibling differentiation.

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 like 'create_target_tag' or 'create_scan', nor does it mention prerequisites or exclusions. It only describes what the tool does, not when it's appropriate, leaving the agent with minimal 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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