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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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self_error_log

Log errors and failures to enable pattern analysis, automatically identifying recurring issues and suggesting insights for resolution.

Instructions

Log an error/failure for pattern analysis. Call AUTOMATICALLY when: bash command fails, wrong assumption discovered, API returns error, config issue found, loop detected, or any mistake occurs. System detects patterns (3+ same category) and suggests insights.

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": {
      "description": "<description>",
      "category": "<category>"
    },
    "name": "self_error_log"
  }
}

Input Schema

NameRequiredDescriptionDefault
fixNoHow it was fixed (empty if unresolved)
tagsNo
contextNoWhat was being done when error occurred
projectNogeneral
categoryYesError category for pattern grouping
severityNomedium
descriptionYesWhat went wrong: symptom, expectation vs reality

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

The description mentions pattern detection and insight suggestion, which implies side effects beyond simple logging. It does not explicitly state persistence or non-idempotency, but the annotations already cover these aspects appropriately.

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, front-loading the core purpose and then listing triggers efficiently. No redundant information, and the structure is logical.

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 logging tool with no output schema, the description adequately covers the purpose, triggers, and expected behavior. It could benefit from an example, but the essentials are present.

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?

The description does not add any parameter-specific guidance beyond what the schema already provides. With schema coverage at 57%, several parameters lack clarity, and the description fails to compensate for this gap.

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: logging errors/failures for pattern analysis. It specifies the exact scenarios in which to call it, making its role unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit triggers for automatic invocation (bash failure, wrong assumption, API error, config issue, loop, or any mistake), leaving no ambiguity about when to use it.

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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