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legionultramax

Harris HawkEye MCP

get_decisions

Retrieve recent logged decisions stored as tribal knowledge. Filter by search term or limit to apply past choices in detection engineering workflows.

Instructions

Get recent logged decisions (tribal knowledge)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum decisions to return (default: 20)
searchNoOptional search term

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
Behavior2/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It only mentions recency and logging status; it does not explain ordering, pagination, search behavior, or whether this is a read-only operation beyond the verb 'Get'.

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, front-loaded sentence with no filler. The parenthetical '(tribal knowledge)' adds useful context without extra words.

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 simple read tool with zero required parameters and full schema coverage, the core purpose is conveyed. However, with no annotations and no output schema, the description leaves ambiguity about what constitutes a 'decision' and how results are ordered or filtered.

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?

The input schema covers both parameters at 100%, including the default limit of 20 and optional search. The description adds no parameter-level semantics, but the schema is sufficient, so baseline 3 applies.

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 ('Get') and resource ('recent logged decisions'), with the parenthetical 'tribal knowledge' clarifying the content type. It distinguishes itself from generic get_* tools, though it does not explicitly differentiate from similar siblings like get_learnings.

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 phrase 'recent logged decisions' implies when to use the tool, but there is no explicit guidance about alternatives or exclusions. With siblings like get_learnings and log_decision nearby, an agent could benefit from clearer routing.

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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