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AI Memory MCP

AI Memory MCP

by moorej2400
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_sync

Synchronize the searchable index with canonical Markdown files, so agents retrieve consistent, up-to-date memory.

Instructions

Update the derived index from canonical Markdown.

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_sync"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
okYes
indexYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations (readOnlyHint=false, destructiveHint=false) already indicate the tool modifies state without being destructive. The description adds meaningful context by specifying the target ('derived index') and the source ('canonical Markdown'), providing domain-specific behavior not captured by annotations.

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 a single, compact sentence (7 words) that immediately states the tool's purpose. Every word earns its place, with no wasted text or repetition of schema details.

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 zero-parameter synchronization tool with an output schema (as indicated in context signals), the description is largely complete. It clearly communicates the operation and source. Minor gaps include not defining what 'derived index' means or when a sync is necessary, but the simplicity of the tool mitigates this.

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

Parameters4/5

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

The tool has zero parameters, so the input schema is essentially empty. With no parameters to document, the description carries the full semantic load by indicating what the tool acts upon ('canonical Markdown'), which clarifies that the input comes from elsewhere rather than explicit arguments.

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 'Update the derived index from canonical Markdown' clearly states a specific action (update) on a specific resource (derived index) with a clear source (canonical Markdown). This distinguishes it from sibling tools like memory_recall and memory_status, which suggest reading and status operations.

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 the tool should be used to sync/update the derived index when canonical Markdown changes, but it does not explicitly mention when to use it versus alternatives, nor does it provide exclusion criteria. Usage is implied rather than directly stated.

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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