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vectra-ai-research

Vectra AI MCP Server

create_entity_note

Add investigation notes to Vectra AI entities (hosts or accounts) for documenting threat analysis and incident response actions.

Instructions

    Add an investigation note to an entity (host or account).
    
    Returns:
        str: Confirmation message with note details.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_idYesID of the entity to add note to
entity_typeYesType of entity to add note to (host or account)
noteYesNote text to add to the entity.

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 but offers minimal behavioral insight. It states the tool 'Adds' a note (implying a write operation) and mentions a return confirmation, but lacks details on permissions, side effects, error conditions, or rate limits. This is inadequate for a mutation tool with zero 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.

Conciseness4/5

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

The description is brief and front-loaded with the core purpose in the first sentence. The second sentence about returns is somewhat redundant given the output schema, but overall it's efficient with minimal waste.

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 mutation nature and lack of annotations, the description is incomplete—it doesn't address behavioral risks or context. However, the presence of an output schema reduces the need to explain return values, and the schema covers parameters well, making it minimally adequate but with clear gaps.

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?

Schema description coverage is 100%, so the schema fully documents all three parameters. The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('Add an investigation note') and target resource ('to an entity (host or account)'), making the purpose immediately understandable. It distinguishes from siblings by focusing on note creation rather than assignments, detections, or listings, though it doesn't explicitly contrast with similar tools since none exist in the sibling list.

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. The description doesn't mention prerequisites, appropriate contexts, or exclusions, leaving the agent to infer usage solely from the tool name and parameters.

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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