mcp-colab-gpu

colab_jobs

Read-only

List all background jobs on your GPU runtime to find job IDs for polling results or canceling tasks.

Instructions

List all tracked background jobs.

Returns a JSON array of job summaries including job_id, status, accelerator type, and timestamps. Use this to find job IDs for colab_poll or colab_cancel.

After listing:

Use colab_poll(job_id) on any job to get detailed results.
Use colab_cancel(job_id) to stop an active job.
Completed/failed/cancelled jobs are auto-cleaned after 5 minutes.

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": "colab_jobs"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.1Jul 30, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already mark readOnlyHint true and destructiveHint false, so the tool is safe. The description adds value by detailing the return format (JSON array with job_id, status, accelerator type, timestamps) and cleanup policy (5 minutes auto-clean). No contradictions.

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 concise with 4 sentences, front-loaded with the main action, and each sentence adds essential information without waste.

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

Completeness5/5

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

Given no parameters, an output schema (implied), and sibling tools, the description fully covers what the tool does, what it returns, how to use its results, and cleanup behavior. It is complete for agent selection and invocation.

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 tool has zero parameters, so the baseline is 4. The description correctly omits parameter information as none exist.

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 tool lists all tracked background jobs, which is a specific verb+resource. It distinguishes from siblings like colab_poll and colab_cancel by noting this tool provides job IDs for them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use this tool (to find job IDs) and provides clear next steps: use colab_poll for details, colab_cancel to stop, and notes auto-cleanup. This guides the agent on context and alternatives.

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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