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Mallory MCP Server

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by malloryai

list_exploitations

Retrieve exploitation data to identify vulnerabilities exploited in the wild, including detection signatures, frequency, and timeframes for security analysis.

Instructions

Get exploitation data

Use this to look up exploitation data when you want to know if vulnerabilities have been exploited in the wild, and who detected the exploitations. This function retrieves a list of exploitation incidents with filtering and sorting options.

The data can help identify:

  • Recent exploitations in the wild

  • Which detection signatures identified the exploitations

  • Frequency of exploitation activities (count)

  • Timeframes of exploitation activities

Args: offset (int, optional): The number of items to skip before starting to collect the result set. Defaults to 0. limit (int, optional): The maximum number of items to return. Minimum value is 1. Defaults to 10 (API default is 100). sort (str, optional): Field to sort by - either 'count', 'created_at', or 'updated_at'. Defaults to 'created_at'. order (str, optional): Sort order - either 'asc' or 'desc'. Defaults to 'desc'.

Returns: Dict[str, Any]: Dictionary containing: - data: List of exploitation records with fields including: - uuid - begins_at - ends_at - count - detection_signature_uuid - detection_signature_name - detection_signature_source - detection_signature_method - created_at - updated_at

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offsetNo
limitNo
sortNocreated_at
orderNodesc

Implementation Reference

  • The handler function for the 'list_exploitations' MCP tool, decorated with @mcp.tool() for registration and implementing the tool logic by forwarding to malloryai_client.exploitations.list_exploitations with pagination and sorting parameters.
    @mcp.tool()
    @handle_api_errors
    async def list_exploitations(
        offset: int = 0,
        limit: int = 10,
        sort: str = "created_at",
        order: str = "desc",
    ) -> Dict[str, Any]:
        """Get exploitation data
    
        Use this to look up exploitation data when you want to know if vulnerabilities have been
        exploited in the wild, and who detected the exploitations. This function retrieves a list
        of exploitation incidents with filtering and sorting options.
    
        The data can help identify:
        - Recent exploitations in the wild
        - Which detection signatures identified the exploitations
        - Frequency of exploitation activities (count)
        - Timeframes of exploitation activities
    
    
        Args:
            offset (int, optional): The number of items to skip before starting to collect the result set.
                Defaults to 0.
            limit (int, optional): The maximum number of items to return. Minimum value is 1.
                Defaults to 10 (API default is 100).
            sort (str, optional): Field to sort by - either 'count', 'created_at', or 'updated_at'.
                Defaults to 'created_at'.
            order (str, optional): Sort order - either 'asc' or 'desc'.
                Defaults to 'desc'.
    
        Returns:
            Dict[str, Any]: Dictionary containing:
                - data: List of exploitation records with fields including:
                    - uuid
                    - begins_at
                    - ends_at
                    - count
                    - detection_signature_uuid
                    - detection_signature_name
                    - detection_signature_source
                    - detection_signature_method
                    - created_at
                    - updated_at
        """
        return await malloryai_client.exploitations.list_exploitations(
            offset=offset, limit=limit, sort=sort, order=order
        )
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool retrieves data (implying read-only, non-destructive) and mentions filtering/sorting options, but lacks details on rate limits, authentication needs, error handling, or pagination behavior beyond offset/limit. It adds some context (e.g., data helps identify specific insights) but doesn't fully cover behavioral traits for a tool with 4 parameters.

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 well-structured with a purpose statement, usage context, bulleted insights, and detailed parameter explanations. It's appropriately sized for a tool with 4 parameters and no output schema. However, the bulleted list could be more concise, and some redundancy exists (e.g., repeating 'exploitation' in insights).

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 4 parameters with 0% schema coverage and no output schema, the description does a good job explaining inputs and returns. It details parameter semantics and provides a clear return structure with field examples. However, it lacks output schema details like data types or nested objects, and doesn't address error cases or behavioral constraints, leaving minor gaps.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It provides detailed semantics for all 4 parameters: offset (items to skip), limit (max items, with min and default noted), sort (fields to sort by), and order (sort direction). It includes default values, constraints (e.g., 'Minimum value is 1'), and clarifies API defaults, adding significant meaning beyond the bare schema.

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 as 'Get exploitation data' and specifies it retrieves 'a list of exploitation incidents with filtering and sorting options.' It distinguishes from siblings like 'get_exploitation' (singular) by indicating it returns multiple records. However, it doesn't explicitly differentiate from 'get_vulnerability_exploitations' or 'list_threat_actors' in terms of scope or focus.

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

Usage Guidelines4/5

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

The description provides clear context for when to use it: 'when you want to know if vulnerabilities have been exploited in the wild, and who detected the exploitations.' It also lists specific use cases (e.g., identifying recent exploitations, detection signatures). However, it doesn't explicitly state when NOT to use it or name alternatives among siblings, such as 'get_vulnerability_exploitations' for a different scope.

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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