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AnkusDrive

by gchen19
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Python
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job_list

Lists all async jobs in the current worker session with status and elapsed time, and shows main-thread queue diagnostics to reveal why a shape optimization is stuck.

Instructions

List every async job this worker session. Returns {count, jobs:[{job_id, kind, status, elapsed_s}]} in submit order, plus main_thread_queue:{queued, ran, failed, drains, pending} — the diagnostic for a shape optimization that looks stuck. Those builds run on the worker's main thread, and that queue is drained once per request: pending high with drains climbing means the work is slow; drains flat means nothing is polling, so nothing is advancing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the return structure and explains the main_thread_queue diagnostic in useful detail, but it does not state whether the operation is read-only, whether it has side effects, or any permission requirements.

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 front-loaded with the core purpose, then efficiently presents the return shape and diagnostic interpretation. Every sentence adds value, and there is no redundant or padding language.

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?

Given zero parameters, no annotations, and no output schema, the description supplies the return shape and queue diagnostic needed to interpret the tool correctly. It omits safety or side-effect context, but for a parameterless list operation it is largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so the baseline score is 4. The description adds no parameter detail because there are none to describe.

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 states a specific verb and resource: 'List every async job this worker session.' It also specifies the return shape, making the tool's purpose unmistakable. However, it does not explicitly differentiate itself from sibling tools like job_status or job_result, which would be needed for a 5.

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 clear context for when the tool is useful: 'the diagnostic for a shape optimization that looks stuck,' with interpretation of pending/drains. It does not explicitly name alternatives or state when not to use it, so it falls short of full routing guidance.

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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