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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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memory_episode_save

Save a narrative episode describing what happened and how, including outcomes, insights, and impact.

Instructions

Save an episode — narrative of WHAT HAPPENED and HOW. Not just facts, but the journey: what was tried, what failed, what worked.

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": {
      "narrative": "<narrative>",
      "outcome": "<outcome>"
    },
    "name": "memory_episode_save"
  }
}

Input Schema

NameRequiredDescriptionDefault
outcomeYes
projectNogeneral
conceptsNoKey concepts involved
narrativeYes2-3 sentence narrative of what happened
key_insightNoThe aha moment, if any
impact_scoreNo0.0-1.0, how significant
approaches_triedNo
frustration_signalsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

The annotations already indicate this is not read-only, not idempotent, and not destructive, so the behavioral profile is partially known. The description adds that it saves an episode, but does not specify side effects like whether it updates an existing episode or always creates a new one. It does not contradict the 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 two concise sentences, front-loading the action and then elaborating on the nuance. Every word contributes to the purpose, with no fluff or irrelevant detail. It is appropriately sized for a tool of this complexity.

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

Completeness3/5

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

The description gives the core purpose and hints at the type of content to include, but it does not explain the required parameters or the meaning of the outcome enum, nor does it address potential side effects or return values. Given the moderate complexity of 8 parameters, this leaves gaps for an agent trying to call it correctly. It is not fully self-contained.

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

Parameters2/5

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

Schema description coverage is 50%, with outcome, project, approaches_tried, and frustration_signals lacking descriptions. The description does not clarify any of these parameters, instead focusing on the overall narrative concept. It fails to compensate for the missing schema details, so parameter semantics are weak.

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 action (save) and the object (an episode) with a specific definition of what an episode is (narrative of what happened and how). It contrasts with just saving facts, which distinguishes it from memory_save. This is a clear, specific purpose.

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 the tool is for recording narrative journeys, including what was tried, failed, and worked, which gives a clear use case. It contrasts with facts, suggesting use when a richer account is needed, but it does not explicitly name alternatives or conditions. This provides moderate guidance but falls short of explicit when-to-use instructions.

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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