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binhnguyen143

IBM QRadar SIEM MCP Server

qradar_get_offense

Retrieve complete details of a specific offense by its ID for investigation and response.

Instructions

Retrieve detailed information about a specific offense by its ID.

Args: offense_id: The ID of the offense to retrieve.

Returns: JSON string containing the offense details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offense_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the return type ('JSON string') but does not disclose whether this is a read-only operation, whether it requires special permissions, what happens if the offense_id does not exist, or any rate-limit or pagination behavior. For a retrieval tool, the lack of error-handling or permission context is a notable gap.

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 short and front-loaded with the main purpose. The Args/Returns structure is clear and wastes no words. It loses one point because the Returns line is slightly redundant with the first sentence ('detailed information' vs 'JSON string containing the offense details'), but overall it is efficient.

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 simplicity (one parameter, no nested objects) and the presence of an output schema, the description is mostly complete. However, with no annotations and no mention of error cases or read-only safety, an agent lacks some context that would be useful for a QRadar API tool. It is adequate for a straightforward get-by-ID operation but not rich.

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 0%, so the description must compensate. It does explain the only parameter ('offense_id: The ID of the offense to retrieve'), which adds meaning beyond the schema's bare type/title. However, it does not specify the ID format (e.g., integer range, where to find it) or any constraints beyond being an integer. With only one parameter, the description is adequate but minimal.

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 states a specific verb ('Retrieve') and resource ('detailed information about a specific offense by its ID'), which clearly distinguishes it from sibling tools like qradar_list_offenses (which lists offenses) and qradar_update_offense (which modifies). It could be slightly stronger by explicitly naming the sibling it contrasts with, but the purpose is unambiguous.

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 implies usage: use this when you need details for a specific offense ID, as opposed to qradar_list_offenses for a collection. However, it does not explicitly state when not to use it or mention alternatives. The context is clear enough for an agent to infer the right scenario, but there is no explicit routing guidance.

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