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mem_save

Save coding session knowledge like patterns, decisions, and insights as searchable memories to aid future sessions.

Instructions

Save a memory during a coding session.

Use this when you discover something worth remembering: patterns, decisions, gotchas, architecture notes, debugging insights, or any knowledge that would help future sessions.

The memory is immediately searchable via mem_search.

Args: content: The memory content to save (be specific and actionable) section: Category heading (e.g. "Security", "Architecture", "Gotchas") project: Project name (auto-detected from cwd if omitted) cwd: Working directory for auto-detection (defaults to os.getcwd())

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNo
contentYes
projectNo
sectionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It mentions that the memory is immediately searchable, but does not disclose any destructive effects, authorization requirements, or whether the tool is idempotent. For a save operation, this is adequate but not thorough.

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 concise and well-structured. It opens with a clear one-liner, then lists usage scenarios in a bullet-like manner, and follows with parameter descriptions. Every sentence adds value without being verbose.

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 that an output schema exists (though not shown), the description need not explain return values. It adequately covers the tool's purpose, parameters, and usage context. It could mention return values or error cases for completeness, but it is sufficient for a save tool.

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?

The schema description coverage is 0%, so the description must compensate. It does so effectively by explaining each parameter: content (be specific and actionable), section (category heading with examples), project (auto-detected), and cwd (defaults to os.getcwd()). This adds significant meaning beyond the schema's basic type and default info.

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's purpose: 'Save a memory during a coding session.' It uses a specific verb and resource, and it distinguishes itself from sibling tools (e.g., mem_search) by noting that saved memories are searchable.

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 provides extensive usage guidance, listing concrete scenarios (patterns, decisions, gotchas, etc.) and stating 'when you discover something worth remembering.' It does not explicitly state when not to use the tool or name alternatives, but the context is 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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