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Claude Memory MCP

Claude Memory MCP

by adeeljames
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Local

save_turn

Archive user messages and AI replies as memory after each response, enabling recollection across conversations.

Instructions

Save one conversation turn to memory. Call this AFTER every response.

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": {
      "user_msg": "<user_msg>",
      "ai_response": "<ai_response>"
    },
    "name": "save_turn"
  }
}

Input Schema

NameRequiredDescriptionDefault
user_msgYesWhat the user said
ai_responseYesWhat the AI replied

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It explains the action (save to memory) and timing, but does not clarify if it overwrites, appends, or any side effects like memory limits. This is adequate but limited.

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 two sentences, with the purpose front-loaded. Every word serves a purpose, with no redundancy or fluff.

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 simple two-parameter tool with no output schema or nested objects, the description is sufficient. It covers what the tool does, when to call it, and the required parameters. Minor gaps like memory behavior don't significantly hinder comprehension.

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%, with both parameters described in the schema ('What the user said', 'What the AI replied'). The description adds no extra meaning beyond this, achieving baseline for high coverage.

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 action ('Save one conversation turn to memory') and specifies the resource ('conversation turn'). It distinguishes itself from siblings (clear_memory, get_context) by focusing on saving turns.

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 explicitly says 'Call this AFTER every response', providing clear timing for use. It does not mention when not to use it or discuss alternatives, but the sibling tools are distinct enough that confusion is unlikely.

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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