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IBM QRadar SIEM MCP Server

qradar_execute_aql_search

Execute AQL queries on QRadar SIEM to search logs and flows, with options to wait for completion and control result size.

Instructions

Execute an AQL (Ariel Query Language) search on QRadar logs or flows.

Args: query: The AQL query expression (e.g. "SELECT sourceip, destinationip, UTF8(payload) FROM events LAST 5 MINUTES LIMIT 50"). wait_for_completion: If True, polls the search until COMPLETED or timeout. If False, immediately returns the search_id. timeout_seconds: Maximum time to wait in seconds (defaults to settings.default_aql_wait_timeout). max_results: Number of records to return once completed (default: 50).

Returns: JSON string containing the search results or the search job status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
max_resultsNo
timeout_secondsNo
wait_for_completionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden and does it well: it states that wait_for_completion polls until COMPLETED or timeout, that false returns a search_id, and that max_results limits returned records. This gives the agent a clear model of the execution lifecycle beyond the bare schema.

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 well-structured with an Args section and Returns section, front-loading the core purpose first. Each parameter is explained in one clear sentence, and the example query adds concrete value without bloating the text.

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 four parameters and no annotations, the description covers the essential behavior, parameters, and return format. It is slightly incomplete in not relating this tool to the search-status/results workflow, but nothing needed to invoke it successfully is missing.

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, and it does fully. It explains query with an example AQL expression, defines wait_for_completion behavior, describes timeout_seconds with its default source, and clarifies max_results semantics including the default value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Execute an AQL (Ariel Query Language) search on QRadar logs or flows.' This clearly identifies the action and object, and it distinguishes the tool from sibling search-status and search-results tools by framing it as the execution step.

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 explains the execution behavior and the wait-for-completion option, which implies when an agent might use the tool. However, it does not explicitly say when to use this tool instead of qradar_get_search_status or qradar_get_search_results, nor does it give explicit when-to-use or exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.