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threat-zone

Threat.Zone MCP Server

by threat-zone

interpret_threat_level

Convert numeric threat level values (0-3) into clear, human-readable descriptions for malware analysis results.

Instructions

Interpret a numeric threat level value from analysis results.

Args: level_value: Numeric threat level (0-3)

Returns: Human-readable threat level description

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
level_valueYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main tool handler: async function that interprets numeric threat level (0-3) to descriptive strings using a dictionary lookup.
    @app.tool
    async def interpret_threat_level(level_value: int) -> str:
        """
        Interpret a numeric threat level value from analysis results.
        
        Args:
            level_value: Numeric threat level (0-3)
            
        Returns:
            Human-readable threat level description
        """
        level_map = {
            0: "Unknown",
            1: "Informative", 
            2: "Suspicious",
            3: "Malicious"
        }
        return level_map.get(level_value, f"Unknown level: {level_value}")
  • The @app.tool decorator registers the interpret_threat_level function as an MCP tool.
    @app.tool
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. It mentions the tool interprets a numeric value into a human-readable description, but does not disclose behavioral traits such as error handling (e.g., for out-of-range values), performance characteristics, or any side effects. 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.

Conciseness5/5

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

The description is appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured 'Args' and 'Returns' sections. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

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's low complexity (1 parameter) and the presence of an output schema (which handles return values), the description is mostly complete. It covers purpose, parameter semantics, and return intent. However, it lacks behavioral details (e.g., error handling), which is a minor gap since annotations are absent and the output schema may not fully describe behavior.

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 meaningful semantics beyond the input schema: it specifies that 'level_value' is a 'numeric threat level (0-3),' clarifying the range and context, while the schema only indicates it's an integer with no description (0% coverage). This compensates well for the low schema coverage, though it could detail format or constraints more explicitly.

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 the tool's purpose: 'Interpret a numeric threat level value from analysis results.' It specifies the verb ('interpret'), resource ('numeric threat level value'), and source ('from analysis results'), distinguishing it from sibling tools like 'get_levels' (which likely retrieves levels) or 'interpret_status' (which interprets status values).

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

Usage Guidelines3/5

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

The description implies usage by mentioning 'from analysis results,' suggesting it should be used after analysis data is obtained. However, it does not explicitly state when to use this tool versus alternatives like 'get_levels' or provide exclusions (e.g., not for non-numeric inputs). The context is clear but lacks explicit guidance on alternatives or prerequisites.

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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