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ai-dememory

ai-dememory

by GonzaloTorreras
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Plan Memory Schedule

memory.schedule_plan
Read-only

Generate platform scheduler commands and reviewed cron export entries for preview without installation.

Instructions

Return platform scheduler commands and reviewed cron export entries without installing them.

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": "memory.schedule_plan"
  }
}

Input Schema

NameRequiredDescriptionDefault
modeNoinstalled
imageNoai-dememory:local
actionNoinstall
platformNo

Output Schema

NameRequiredDescriptionDefault
modeYes
rootYes
imageYes
actionYes
commandsYes
platformYes
scheduleYes
cron_entriesYes
next_actionsYes
writes_filesYes
runs_commandsYes
mutates_systemYes
installs_schedulesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the main behavioral trait of non-destructiveness. The description adds 'without installing them', reinforcing this. No contradictions exist, but the description does not elaborate on what 'reviewed cron export entries' entails or additional side effects.

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 a single sentence of 12 words, concise and front-loaded. It avoids fluff, but could be slightly more structured to separate the returned items from the action caveat.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 4 undocumented parameters and lack of usage context, the description is incomplete. The tool has an output schema, which may help, but the input parameters remain a black box. More details on platform-specific behavior or typical use cases are needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 4 parameters with 0% schema description coverage. The description provides no explanation for any parameter (mode, image, action, platform), leaving the agent without guidance on how to set them. With coverage so low, the description should compensate but does not.

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 the tool returns scheduler commands and cron entries without installing them. The verb 'Return' and resource 'platform scheduler commands and reviewed cron export entries' indicate a preview function. However, it could better differentiate from siblings like memory.schedule_status, which likely shows current schedule status.

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 implies usage for previewing a plan before installation via 'without installing them', but it provides no explicit when-to-use or when-not-to-use guidance nor comparisons to sibling tools. The agent must infer context.

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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