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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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workflow_track

Record workflow execution outcomes—success, failure, partial, or aborted—with optional notes, duration, and error details. Aggregates update automatically for tracking.

Instructions

Record a workflow execution outcome. Outcome ∈ {success|failure|partial|aborted}. Aggregates update automatically.

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": {
      "workflow_id": "<workflow_id>",
      "outcome": "<outcome>"
    },
    "name": "workflow_track"
  }
}

Input Schema

NameRequiredDescriptionDefault
notesNo
outcomeYes
duration_msNo
workflow_idYes
error_detailsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

The description mentions that 'Aggregates update automatically,' which hints at side effects. It does not contradict the annotations (readOnlyHint false, etc.), but it could be more explicit about whether the operation is append-only or modifies existing records.

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 extremely concise and to the point, using a compact notation for the outcome enum. Every word adds value, and there is no redundant information.

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?

For a simple logging tool, the description provides the essential purpose and a key side effect. However, it lacks details on parameter semantics and any caveats (e.g., whether the workflow must already exist). This leaves some gaps for an agent unfamiliar with the tool.

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 explains the 'outcome' parameter partially by listing its enum values, but provides no explanation for 'workflow_id', 'notes', 'duration_ms', or 'error_details'. With 5 parameters and 0% schema description coverage, the description does not adequately clarify the meaning or usage of most parameters.

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 ('Record') and the object ('workflow execution outcome'), and enumerates the valid outcome values. It is immediately obvious what the tool does and how it differs from sibling workflow tools.

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 explains the core action but does not explicitly state when to use this tool versus alternatives like workflow_learn or workflow_predict. The context signals and sibling names imply it is for logging outcomes, but explicit guidance is absent.

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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