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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_skill_update

Update skill records with success metrics, notes, and new steps to refine and track performance.

Instructions

Record skill usage or refine a skill. Updates success rate and metrics.

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": {
      "skill_id": "<skill_id>",
      "success": "<success>"
    },
    "name": "memory_skill_update"
  }
}

Input Schema

NameRequiredDescriptionDefault
notesNo
successYesWas the skill application successful?
skill_idYesSkill ID
new_stepsNoAdditional steps to add
new_anti_patternNoAnti-pattern learned from failure

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?

The description explicitly states that it 'Updates success rate and metrics', making the side effect transparent. Annotations reinforce non-readonly/non-destructive behavior, but details on whether updates append or overwrite are not disclosed.

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 concise—two sentences—with no redundancy. It directly states the action and effect without extraneous detail, fitting the tool's simple scope.

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?

Given the tool's straightforward nature and no output schema, the description covers essential aspects: what it does and what it affects. The requirement of skill_id and success is implicit. It does not discuss edge cases or relationships with other skill-related tools, but this is not critical for a basic update operation.

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?

Of the 5 parameters, 4 have descriptive text (skill_id, success, new_steps, new_anti_pattern) with clear meanings. The 'notes' parameter lacks a description but appears self-explanatory. 80% coverage with clear field names supports effective use.

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: 'Record skill usage or refine a skill. Updates success rate and metrics.' It distinguishes itself from generic memory save/update tools by explicitly targeting skill usage and metrics.

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 (when recording or refining a skill) but does not explicitly state when to prefer this over siblings like memory_save or memory_update. It lacks explicit when-not-to-use guidance, though the phrase 'skill usage' provides some 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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