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lflabs-memory

lflabs-memory

by LF-Labs-Inc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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access_status

Check which parts of a user's long-term memory a given AI client can access, including work-only, personal, or hidden sections; read-only for visibility audits.

Instructions

What an AI agent may see in the user's long-term memory, in plain words: everything, work only, nothing personal, or which sections it never sees. client: an agent (codex, cursor, claude-desktop, windsurf, gemini, copilot, chatgpt, desk for Claude Code, unknown for new agents); omit it for yourself. Read-only.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "access_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
clientNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.7

TDQS

A3.8/5.0
Behavior4/5

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

There are no annotations, so the description must carry behavioral disclosure. It clearly states that the operation is read-only and explains the kinds of visibility status the agent may encounter, including that new agents may appear as 'unknown'. It does not cover permission or rate-limit behavior, but it provides solid behavioral context for a simple read-only status tool.

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 front-loaded with the core purpose and is generally efficient. The client explanation is a long parenthetical list, but every part is useful and there is no obvious filler.

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 one optional parameter, and has an output schema, so the description does not need to explain return formatting. It covers purpose, possible statuses, the read-only nature, and client semantics; the main remaining gap is explicit guidance about choosing this tool over access_change or recall.

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 must explain the single optional parameter. It does so well by defining the client parameter as an agent identity, listing example agents, explaining 'unknown' for new agents, and explicitly saying to omit it for yourself.

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 identifies the resource: what an AI agent may see in the user's long-term memory, and enumerates the possible visibility states. It does not use a precise verb such as 'report' or 'check', and it does not name a sibling to distinguish itself from, which keeps it below 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states that the tool is read-only, which implicitly contrasts with a mutating sibling such as access_change, but it never says when to use this tool versus alternatives. It gives client-parameter guidance but no explicit tool-selection guidance or exclusions.

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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