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threat-zone

Threat.Zone MCP Server

by threat-zone

get_submission_network_threats

Retrieve network threats detected for a specific malware submission to analyze attack vectors and security risks.

Instructions

Get all network threats for a specific submission.

Args: uuid: Submission UUID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uuidYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'get_submission_network_threats' tool. It is registered via the @app.tool decorator and fetches network threats data from the ThreatZone API for a given submission UUID.
    @app.tool
    async def get_submission_network_threats(uuid: str) -> Dict[str, Any]:
        """
        Get all network threats for a specific submission.
        
        Args:
            uuid: Submission UUID
        """
        return await get_client().get(f"/public-api/get/submission/{uuid}/threats")
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 action is to 'Get' data, implying a read-only operation, but doesn't specify permissions, rate limits, or what 'all network threats' entails (e.g., format, pagination). This leaves significant gaps 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by a concise 'Args' section. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.

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

Completeness4/5

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

Given the tool has an output schema (which handles return values), 1 parameter with low schema coverage, and no annotations, the description is reasonably complete. It covers the purpose and parameter semantics but lacks behavioral details and usage guidelines, which are minor gaps in this context.

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 meaning by specifying that the 'uuid' parameter corresponds to a 'Submission UUID', clarifying its purpose beyond the schema's generic 'string' type. With 0% schema description coverage and only 1 parameter, this compensates adequately, though it could provide more context on UUID format or sourcing.

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 verb 'Get' and the resource 'all network threats for a specific submission', making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like get_submission_indicators or get_submission_iocs, which might overlap in threat-related data retrieval.

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 is provided on when to use this tool versus alternatives. With many sibling tools like get_submission_indicators and get_submission_iocs that might retrieve similar threat-related data, the description lacks context on specific use cases or exclusions, leaving the agent to infer usage.

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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