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AliYar-Khan

light-agent-memory-mcp-server

by AliYar-Khan

Save Learning

memory_learning_save

Record session learnings, discovered solutions, and bugs to a persistent, searchable memory. Retain insights and project context for future retrieval across AI agent sessions.

Instructions

Record a session learning — solutions discovered, bugs encountered, insights gained.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoTags for categorization (e.g. ['debugging', 'performance'])
titleYesShort description of the learning
contentYesDetailed notes about what was learned
project_nameNoAssociated project name (optional)
Behavior3/5

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

Annotation shows this is a mutating, non-idempotent, non-destructive operation. The description adds what kinds of learning should be captured without claiming destructive effects, but it does not disclose whether repeated calls create new records or overwrite existing ones.

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 short, dense sentence carries all the essential purpose and content guidance. No filler, no repetition of schema information, and the central action verb is front-loaded.

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 is simple, has fully documented parameters, and the annotations declare the mutating but non-destructive custom strength. With no output schema, the description does not need to explain return values; the one meaningful gap is explicit caller guidance for distinguishing this tool from memory_save.

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%, and each parameter already has a meaningful description. The tool description provides illustrative examples of what the content might include, but it does not add additional semantic constraints beyond the schema.

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 the specific verb 'Record' on the resource 'session learning' and includes concrete content examples such as solutions, bugs, and insights. It is clear and distinct from sibling save tools for projects/preferences, though it does not explicitly name an alternative.

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 sentence signals when to use it: after a session a learning should be recorded. The domain terminology and sibling set make the intended use obvious, but there is no explicit when not to use this tool or a named alternative.

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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