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vectra-ai-research

Vectra AI MCP Server

get_detection_count

Count security detections in Vectra AI by applying filters like date range, category, state, IP address, or key asset targeting to analyze threat data.

Instructions

    Get the total count of detections matching the specified criteria.

    Returns:
        str: Count of detections matching the criteria.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_dateNoFilter by start date (YYYY-MM-DDTHH:MM:SS)
end_dateNoFilter by end date (YYYY-MM-DDTHH:MM:SS)
detection_categoryNoFilter by detection category
stateNoFilter by detection state (active, inactive, fixed, filteredbyai, filteredbyrule). Default is 'active' which returns only currently active detections.active
detection_nameNoFilter by detection name. Can also perform partial word match
src_ipNoFilter by source IP address of the host that generated the detection.
is_targeting_key_assetNoFilter for detections targeting a key asset. Defaults to 'False'. Set to 'True' to filter for detections that are targeting key assets. To get all detections regardless of key asset targeting, search for both True and False values.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states the basic purpose and return type. It doesn't mention whether this is a read-only operation (implied but not explicit), potential rate limits, authentication requirements, or how the count is calculated (e.g., real-time vs cached). The description adds minimal behavioral context beyond the basic function.

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 concise with two sentences: one stating the purpose and one describing the return value. Both sentences earn their place by providing essential information. The structure is front-loaded with the main purpose first. There's no unnecessary verbiage or redundancy.

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 tool's moderate complexity (7 parameters, filtering logic) and the presence of an output schema (implied by 'Returns: str'), the description is minimally adequate. It states what the tool does and what it returns, but doesn't provide context about performance, limitations, or relationship to sibling tools. With no annotations and rich parameter schema, the description could do more to guide usage.

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

Parameters3/5

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

The description mentions 'specified criteria' but doesn't elaborate on what those criteria are. However, with 100% schema description coverage, all 7 parameters are well-documented in the schema with clear descriptions, defaults, and formats. The description adds no parameter semantics beyond what the schema already provides, meeting the baseline for high schema coverage.

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: 'Get the total count of detections matching the specified criteria.' This is a specific verb+resource combination (get + count of detections). However, it doesn't explicitly differentiate from sibling tools like 'list_detection_ids' or 'list_detections_with_basic_info', which might also return counts or filtered lists.

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. With siblings like 'list_detection_ids', 'list_detections_with_basic_info', and 'list_detections_with_details' available, there's no indication whether this tool is preferred for count-only queries or how it differs functionally from those listing tools.

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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