lflabs-memory

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remember

Save facts, preferences, and project decisions across sessions so AI apps can recall user context later. Each call stores one self-contained memory with an optional kind.

Instructions

Save something worth knowing in future sessions: who the user is, preferences, feedback on how to work, project decisions, people, pointers. One fact per call, self-contained (it will be read with no surrounding context): a first line that summarises it, then the detail. kind is one of user | feedback | project | reference | note; without one it is filed where this app can see it again. Near-duplicates update the old memory. Facts only: a rule, an instruction or anything with a command or link is kept for recall only (never added to prompts automatically) until the user confirms it, and a command piped to a shell or words that keep something from the user are refused.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "content": "<content>"
    },
    "name": "remember"
  }
}

Input Schema

Name	Required	Description	Default
`kind`	No
`content`	Yes
`project`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.7Nov 10, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden and does well: it discloses one-fact-per-call storage, self-contained formatting, near-duplicate updating, default filing when kind is omitted, recall-only treatment for instructions and commands, and outright refusal for dangerous or secretive content. It does not discuss permissions, side effects outside memory storage, or response behavior, though an output schema exists.

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 dense but front-loaded with the core purpose and then layers in content format, kind semantics, duplicate behavior, and safety restrictions. Every sentence is relevant, though the single long paragraph could be easier to scan with clearer structure.

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?

For a memory-saving tool with three parameters and an output schema, the description covers the important behavioral rules, content expectations, refusal cases, and kind semantics. The main remaining gaps are the unexplained project parameter and the lack of explicit sibling routing, but otherwise it is sufficiently complete.

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 0%, so the description must compensate. It explains the kind values and default filing behavior, and it describes the content format in useful detail. However, the project parameter is never mentioned, leaving one of three parameters undocumented.

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 and resource: save a fact worth knowing for future sessions, with concrete examples like user identity, preferences, feedback, project decisions, people, and pointers. It clearly distinguishes saving from retrieval in spirit, but it does not explicitly name recall, forget, or other siblings as alternatives.

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?

It gives clear usage context: use it for durable facts that future sessions should know, one fact per call, self-contained. It also provides when-not guidance, including that rules, instructions, commands, or links are kept for recall only and that shell-piped commands or requests to hide things from the user are refused. It stops short of explicitly routing the agent to recall, forget, or other 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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