Lists dataset folders in ai-toolkit's datasets directory, returning full paths and file counts for selecting a training dataset path when creating a job.
List dataset folders in ai-toolkit's datasets folder, with full paths and file counts.
Use the path value as datasets.0.folder_path when creating a job.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered; the description adds that results carry full paths and file counts, which is real behavioral context. It doesn't discuss ordering, empty-state behavior, or recursion, but an output schema exists to carry return shape.
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?
Two sentences, no filler. What it returns comes first, the actionable follow-up second, which matches the order an agent needs them.
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?
With an output schema present and no inputs, the description only needs to establish scope and the path-to-job linkage, and it does both. A note on whether nested folders are included or how results are ordered would close the remaining gap.
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?
The tool takes zero parameters, so there is nothing for the description to disambiguate at the argument level. Baseline 4 applies; the description correctly adds no phantom parameter language.
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?
Specific verb (list) and resource (dataset folders) scoped to ai-toolkit's datasets folder, and it names the returned fields (full paths and file counts). An agent can distinguish it from list_jobs, list_job_outputs, and create_dataset without opening any schema.
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 second sentence gives concrete downstream usage: take the `path` value and use it as `datasets.0.folder_path` when creating a job. That ties the tool to the create_job workflow. It stops short of explicit when-not guidance, but for a zero-argument read-only lister there is little ambiguity to resolve.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.