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intruder-mcp

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create_targets

Add one or more host addresses to create targets for security assessment.

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler for 'create_targets'. Decorated with @mcp.tool(), it converts a list of addresses to a list of dicts and delegates to the API client.
    @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 API client method 'bulk_create_targets' that filters out already-existing targets and POSTs new ones to /targets/bulk/
    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}]
  • The Target Pydantic model used as the return type for bulk_create_targets.
    class Target(BaseModel):
        id: int
        address: str
        has_api_schemas: bool
        has_authentications: bool
        license_type: Optional[LicenseTypeEnum] = None
        tags: Optional[List[Optional[str]]] = None
        target_status: TargetStatusEnum
  • The TargetCreateRequest Pydantic model used for creating targets (though bulk_create_targets uses flat dicts instead).
    class TargetCreateRequest(BaseModel):
        address: str
        tags: Optional[List[str]] = Field(None, min_items=1, max_length=40)
        target_authentication: Optional[TargetAuthenticationsRequest] = None
  • The tool is registered as an MCP tool via the @mcp.tool() decorator within the main() function of server.py.
    @mcp.tool()
    async def create_targets(addresses: List[str]) -> str:
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as side effects, permissions, idempotency, or rate limits. For a creation tool, more context is needed.

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 concise and front-loaded with the main action. The docstring format is efficient, though a bit terse.

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 the output schema exists, return values are covered. However, the description lacks context about target constraints, validation rules, or limits, leaving room for ambiguity.

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 description adds value beyond the schema by explaining the 'addresses' parameter with examples for single and multiple targets. Schema coverage is 0%, so this is helpful.

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 'Create one or more targets' with a specific verb and resource, and it is distinct from sibling tools like delete_target or list_targets.

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?

No guidance on when to use this tool vs alternatives (e.g., create_target_tag) or prerequisites. The description only states the action without 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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