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legionultramax

Harris HawkEye MCP

list_by_data_source

Find detection rules that require a specific data source or log type, such as Sysmon Events or AWS CloudTrail, to identify relevant rules for your environment.

Instructions

Find detection rules that require a specific data source or log type (e.g., Sysmon Events, Process Creation Events, DeviceProcessEvents).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results (default: 50)
data_sourceYesData source or log type name (e.g., Sysmon Events, DeviceProcessEvents, AWS CloudTrail)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations provided, the description must carry the behavioral disclosure burden itself. The verb 'Find' reasonably signals a read-only query rather than a mutation, but the description does not disclose matching semantics, case sensitivity, pagination behavior, or whether the result is a list of full rules or summaries.

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 a single, clear sentence with no filler. The core action and target resource are front-loaded, and the examples are useful without adding unnecessary length.

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?

The tool is simple, with two documented parameters and one required field, so the description is arguably sufficient to invoke it. However, it lacks an output shape description and does not address the closely related list_by_logsource sibling, leaving an agent without enough context to choose correctly among similar detection-listing tools.

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 already documents both parameters. The description adds the requirement concept and examples, but those examples largely mirror the schema's own examples, so it provides little additional meaning beyond the structured input definitions.

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 uses a specific verb and resource: 'Find detection rules that require a specific data source or log type.' It clearly states what the tool does and gives concrete examples. However, it does not differentiate from the similarly named sibling list_by_logsource, which appears to serve a nearly identical purpose.

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?

The description gives no guidance on when to use this tool versus alternatives such as list_by_logsource or search_detections. It only describes the action without stating exclusions, preconditions, or the distinguishing conditions that should route an agent here.

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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