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sentinelone-mcp

sentinelone-mcp

by merabytes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Process Pending Alerts

process_pending_alerts

Process pending XDR alerts by fetching them from Elasticsearch and creating them via GraphQL, after a prior login refresh for visibility.

Instructions

Process pending XDR alerts from Elasticsearch and create them via GraphQL. Requires prior refresh_login for XDR visibility cookies.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "process_pending_alerts"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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 creates GraphQL entries and depends on prior authentication, but it does not explain what happens to the original Elasticsearch records, whether the operation is idempotent, or what side effects the creation may have.

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?

Two compact sentences deliver the core action, data source, destination, and a required prerequisite with no filler. The most important operational constraint is placed up front.

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?

For a tool with no parameters and an output schema, the description is reasonably complete, but it lacks details about post-processing effects, idempotency, and failure behavior. Since there are no annotations, these gaps are left entirely to the agent's inference.

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 input schema has no parameters, so there are no parameter semantics to document. The description appropriately focuses on prerequisites and processing behavior instead.

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: process pending XDR alerts from Elasticsearch and create them via GraphQL. It identifies a specific resource and destination, but does not explicitly contrast itself with sibling tools like get_alerts or refresh_login.

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 gives a clear prerequisite by stating that refresh_login must be called first for XDR visibility cookies. However, it does not explain when to use this tool versus alternatives such as get_alerts or mark_threat_resolved, leaving the decision partly 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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