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workbench_watch

Set up persistent local monitoring for background jobs, checking progress without extra model calls. Disable watch anytime while keeping generation active.

Instructions

Configure persistent local task monitoring. Checks happen in the worker without repeated model calls. Stopping monitoring does not cancel generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes
enabledNo
intervalNo
format_nameNoauto
auto_downloadNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose two non-obvious behavioral traits: monitoring checks run in the worker without repeated model calls, and stopping monitoring does not cancel generation. It could add more about response behavior or side effects, but the key operational and safety-relevant behaviors are surfaced.

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 three short sentences with no filler, front-loaded with the purpose, and every sentence adds distinct behavioral or contextual information. It is appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With five parameters and no output schema, the description is not complete enough for an agent to configure the tool confidently. It omits parameter semantics such as interval units, format_name values, and auto_download behavior, and says nothing about the return value or how existing watches are affected.

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 0%, so the description must compensate for the five parameters, but it does not explain job_id, enabled, interval, format_name, or auto_download. Phrases like 'stopping monitoring' weakly map to enabled, but no parameter receives meaningful semantic clarification.

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 uses a specific verb and resource ('Configure persistent local task monitoring') and makes the tool's role clear compared to one-time status checks or listing watches. It does not explicitly name a sibling tool, but the wording distinguishes it from workbench_status and workbench_watches well enough.

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?

The description gives clear context: this is for persistent, low-overhead monitoring because checks happen in the worker without repeated model calls. It also warns that disabling monitoring is not a cancellation action, which is important guidance for when to stop using it. It does not explicitly enumerate alternatives or exclusions, so it is not a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.