Lists stored Kaggle or Colab jobs with optional state and provider filters and a result limit; returns mock test fixtures, not live account data.
List stored jobs. Mock data are TEST FIXTURES, not a live account.
This does not call a provider and does not submit. limit defaults to
50. Pass null to return every match.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| state | No | ||
| provider | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and usefully discloses that the data are mock test fixtures, no provider call occurs, and no submission happens. It also states the limit default and null behavior. It stops short of explicitly declaring read-only semantics or describing filter behavior, but the key operational caveats are present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the purpose, then adds the essential fixture/no-provider/no-submit caveats, then explains limit behavior. Every sentence contributes useful information, and there is no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values need not be described. The description covers the crucial mock-data caveat and limit behavior, but it omits meaning for the state and provider filters in a schema with zero parameter descriptions. That leaves clear gaps for correct invocation, though the tool remains minimally usable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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. It explains only the limit parameter (default 50, null returns every match) and leaves state and provider entirely unexplained, so two of three parameters remain semantically opaque.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 stored jobs.' It also scopes the data source as mock/test fixtures and says it does not call a provider or submit, which separates it from provider-backed or submission tools. It does not explicitly name a sibling such as gpu_list_jobs, so it falls short of full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear usage boundary by stating the data are test fixtures, not a live account, and that the tool does not call a provider or submit. However, it does not explicitly say when to use this instead of alternatives such as gpu_list_jobs or l2kc_list_jobs, leaving the agent to infer the routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.