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threadlinqs-cmd

Intel Threadlinqs MCP

Get Detection Detail

get_detection_detail
Read-onlyIdempotent

Retrieve complete details for a detection rule by its ID, including the full query text (SPL/KQL/Sigma), metadata, and the threat it maps to.

Instructions

Get the full detail for one detection rule by its ID, including the complete query text (SPL/KQL/Sigma), metadata, and the threat it maps to.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detection_idYesDetection ID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
nameNo
queryNo
severityNo
kql_queryNo
threat_idNoPass to get_threat.
sigma_ruleNo
threat_titleNo
mitre_mappingNo
detection_typeNo
false_positivesNo
Behavior3/5

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

Annotations provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, which fully cover the safety profile. The description adds value by specifying the returned content depth (complete query text, metadata, threat mapping) beyond simple read semantics. However, it doesn't discuss behaviors like what happens with an invalid/unknown detection_id, rate limits, or whether the full query text is ever truncated.

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?

One tightly-written sentence that covers purpose, scope, and returned content without waste. Every element earns its place — the query-format enumeration (SPL/KQL/Sigma) is valuable detail packed into few words.

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?

The tool has an output schema, extensive annotations, and a single well-documented parameter, so the description's job is modest. It competently communicates what's returned (full query text, metadata, threat mapping). Minor gap: no mention of how a missing/unknown ID is handled or fallback behaviors, but for a read-only single-fetch tool with strong structured metadata, this is largely sufficient.

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 coverage is 100% and the single parameter detection_id is simply documented as 'Detection ID'. The description reinforces the parameter's role (the ID of the detection to fetch) but adds no format, source, or usage detail beyond what the schema provides. With full schema coverage, baseline 3 is appropriate.

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 clearly states the tool fetches full detail for a single detection rule by ID, enumerating specific content included (query text in SPL/KQL/Sigma, metadata, threat mapping). The verb 'Get' plus resource 'detection detail' is specific and distinct from siblings like get_detections (which lists detections) and search_detections (which searches).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this is for retrieving a single detection when you have a specific ID in hand, contrasting implicitly with get_detections (bulk listing) and search_detections (searching). It doesn't explicitly name alternative tools for when NOT to use it, but the single-ID parameter and 'full detail' framing make context reasonably clear.

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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