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judge_propose

Propose a typed workflow or node judge. Use when an update needs quality checks. Do not use when you need to approve or change a frozen judge. Next step: call metrics_propose and ask a person to approve both. Limits: the judge stays proposed until a person approves it. Example: {"name":"Invoice quality","workflowId":"invoice-flow","config":{}}.

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": {
      "name": "<name>",
      "workflowId": "<workflowId>",
      "config": "<config>"
    },
    "name": "judge_propose"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYes
configYes
nodeIdNo
workflowIdYesLogical workflow ID
descriptionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=false, destructiveHint=false and openWorldHint=false, so the description usefully adds lifecycle semantics: the judge remains 'proposed' and has no effect until a person approves it, and a second proposal call is required. It omits anything about permissions, rate limits, or what the proposal response contains, so it is strong but not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Usage rule, exclusion, next step, and limit are front-loaded in short sentences with no filler. The trailing raw JSON example is a bit bulky but does anchor the required top-level shape.

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 mutation tool with a large nested schema and no output schema, the description covers the lifecycle and follow-up well but leaves the config envelope — the part an agent will most likely get wrong — undocumented. No output schema means the description could also say what a successful proposal returns.

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 only 20% across 5 parameters with a deeply nested config (prompt, model, scope, provider, questions with three variants, samplingRate, scoreConfigs), and the description explains none of it. The inline example shows 'config':{} with no guidance, which is misleading given that config requires a prompt field.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource: 'Propose a typed workflow or node judge,' which tells an agent exactly what object is created and that both workflow-level and node-level judges exist. It is distinguishable from siblings such as llm_judge_create or workflow_judge_set, though it never explicitly names those siblings as alternatives.

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?

It gives a positive trigger ('Use when an update needs quality checks') and a negative one ('Do not use when you need to approve or change a frozen judge'), plus an explicit next step (metrics_propose + human approval). It stops short of naming the concrete alternative tool for the excluded case, leaving the agent to infer workflow_judge_set or llm_judge_update.

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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