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l2kc_list_accounts

Lists configured Kaggle and Colab accounts with quota status, excluding credential values, so agents can plan provider usage.

Instructions

List accounts. Mock data are TEST FIXTURES, not a live account.

Credential values are not included. Quota exhaustion does not rotate to another account of the same provider.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior3/5

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

No annotations exist, so the description carries the whole burden. It does add real behavioral context: data is mock/test fixtures rather than live accounts, credentials are omitted from the output, and quota exhaustion does not fail over to another account of the same provider. It says nothing about pagination, ordering, or whether the call is side-effect free.

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?

The action is front-loaded in the first clause and the caveats follow in short, waste-free sentences. The fixture sentence is slightly awkwardly phrased but earns its place by disambiguating the mock nature of the data.

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?

An output schema exists, so return-value detail is not required, and a zero-param list tool is simple. However, given 28 similarly named sibling tools, the description omits the one thing an agent most needs here: how this differs from gpu_list_accounts and when to pick each.

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 schema has zero parameters, so there is nothing to clarify; baseline 4 applies. The description correctly implies the tool takes no filters.

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

Purpose3/5

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

States a specific verb and resource ("List accounts"), so the basic action is unambiguous. But with 28 siblings including gpu_list_accounts, it never explains what distinguishes the l2kc variant (mock/test-fixture accounts) from the gpu one, leaving the agent to guess which to call.

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

Usage Guidelines2/5

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

No statement of when to use this tool, when not to, or which sibling to prefer. The test-fixture caveat hints at a mock context but is not framed as a usage rule and no alternative is named.

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