sentinelone-mcp

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Run Dv Query

run_dv_query

Execute raw Deep Visibility S1QL queries to retrieve matching endpoint events, handling initialization, polling, and result retrieval for security investigations.

Instructions

Run a raw Deep Visibility S1QL query (init → poll → events). Example: storyline = "ABC123" AND event.type = "Process Creation".

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "run_dv_query"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`query`	Yes
`tenant`	No
`to_date`	No
`from_date`	No
`timeout_seconds`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 2, 2026

TDQS

B3.2/5.0

Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals that the tool works as a stateful sequence ('init → poll → events'), which is a meaningful behavioral trait beyond the schema. It does not mention side effects, permissions, or error behavior, but the lifecycle disclosure adds real value.

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 compact and front-loaded: one clear verb-and-resource sentence followed by a useful example. There is no filler, and every sentence contributes to understanding.

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

Completeness2/5

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

Even though an output schema exists for return values, the description remains incomplete for a 6-parameter tool: it omits parameter semantics, date/time handling, tenant behavior, timeout implications, and any guidance on when to choose this raw-query tool over siblings. An agent would likely need to inspect system conventions or make unsafe assumptions.

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

Parameters2/5

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 only illuminates the 'query' parameter through an S1QL example, leaving limit, tenant, from_date, to_date, and timeout_seconds unexplained in both schema and description. The agent would have to guess formats and defaults for most parameters.

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 ('Run a raw Deep Visibility S1QL query') and adds a lifecycle hint ('init → poll → events') plus an example query. It is scoped to Deep Visibility/S1QL, which helps distinguish it from higher-level tools, though it does not explicitly name or contrast sibling tools like xdr_query.

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 explicit guidance is given about when to use this tool versus alternatives such as xdr_query, get_storyline_events, or purple_ai_query. The word 'raw' implies direct querying is the use case, but the description leaves the selection criteria implicit.

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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