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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_warmup

Idempotent

Pre-load the FastEmbed model and open the vector store to eliminate startup latency for the first save or search operation.

Instructions

v11.0: pre-load FastEmbed model and open the vector store, so the first save/search after process start doesn't pay model-load latency.

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_warmup"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

The description discloses the primary side effects: pre-loading a model and opening the vector store. It does not mention potential errors or whether prior setup is required, but the idempotentHint annotation aligns with the described warming behavior and there is no contradiction.

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 that conveys the action, the resource involved, and the reason for the action. Every word contributes meaning, and no unnecessary detail is included.

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, parameterless warm-up utility, the description provides the essential context: what is loaded, what is opened, and why it matters. It could mention failure modes or prerequisites, but the current level is complete enough for the tool's simplicity.

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?

There are no parameters, so parameter-level semantics are trivially covered. The description adds no parameter information because none exists; a baseline score of 4 is appropriate for a zero-parameter tool.

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 purpose: pre-load the FastEmbed model and open the vector store. It also explains the intended benefit—avoiding model-load latency on the first save/search—which is specific and actionable.

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 implies when to use the tool: after process start and before the first save/search. It does not explicitly mention alternatives, but with zero parameters and a focused warm-up purpose, the guidance is sufficiently clear.

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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