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ArkaAiAdmin

Agentic Memory

by ArkaAiAdmin

memory_save

Save a memory note as a Markdown file with SQLite indexing for persistent, cross-session recall by AI agents. Specify content, category, tags, and importance level.

Instructions

Save a memory note with sensible defaults.

Args: content: The memory content (markdown). category: lessons / decisions / projects / preferences / sessions (default: lessons). title_slug: URL-friendly slug (auto-generated if empty). tags: Optional keyword tags. pinned: Pin to hot tier (default False). importance: 1-5 (default 3). is_global: Save to global memory (default False). safety_wiring: If False, skip prompt-injection scanning (default True). Set to False for legitimate structured content with section headers and requirement keywords that may trigger false positives.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
pinnedNo
contentYes
categoryNolessons
is_globalNo
importanceNo
title_slugNo
safety_wiringNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description fully details each parameter's behavior, including defaults and special notes (e.g., auto-generation of title_slug, safety_wiring scanning). It does not describe the return value or side effects, but an output schema exists, so this is acceptable.

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 longer but well-organized with a summary sentence followed by parameter details in a bullet-style list. Every sentence adds value, though it could be slightly more terse.

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 the parameter count (8, 1 required) and the presence of an output schema, the description covers all necessary aspects for effective use. It explains parameter behaviors and constraints, leaving no critical gaps for an agent to infer.

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 compensates excellently. Each parameter is explained with its type, default, and constraints (e.g., category options, importance range, safety_wiring purpose). This adds significant meaning beyond the schema's raw JSON.

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 clearly states 'Save a memory note with sensible defaults,' providing a specific verb and resource. It lists all parameters but does not explicitly differentiate from sibling tools like memory_note or memory_learn, though the purpose is distinct enough.

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 includes guidance on safety_wiring parameter (when to set False) but lacks explicit instructions on when to use this tool versus alternatives. It implies usage through defaults but does not provide context for choosing this tool over siblings.

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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