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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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workflow_predict

Read-onlyIdempotent

Predict workflow outcomes by providing a workflow ID or trigger keyword. Returns success probability and average duration based on Laplace-smoothed historical data.

Instructions

Predict outcome (success probability, avg duration) for a workflow by id OR by trigger keyword. Uses Laplace-smoothed success rate.

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": "workflow_predict"
  }
}

Input Schema

NameRequiredDescriptionDefault
projectNo
triggerNo
workflow_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare read-only and idempotent behavior. The description adds meaningful transparency by revealing the Laplace-smoothed success rate and the nature of the prediction (probability and duration), which is not captured in 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?

Extremely concise and well-structured. Two sentences contain all essential information without any filler or redundancy.

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 tool with three parameters and no output schema, the description provides enough context to understand the core functionality and invocation modes. It lacks details on parameter requirements (e.g., project) and output format, but these are partially compensated by the simplicity of 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?

No parameter descriptions exist in the schema. The description hints that workflow_id and trigger are alternative identifiers, but project is completely unexplained. Coverage is insufficient to fully understand parameter roles and constraints.

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?

Clearly states the action ('Predict outcome'), the resource ('workflow'), the specific outputs ('success probability, avg duration'), and the two identification methods ('by id OR by trigger keyword'). No ambiguity.

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?

Provides limited guidance on when to use this tool versus alternatives. It mentions two identification modes but does not explain when to prefer one or when to use this tool over similar siblings like workflow_learn or workflow_track.

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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