dataset_validateValidate local JSON/JSONL datasets and pinpoint the exact indexes of malformed records, so training data errors are caught before a fine-tuning run starts.
Validate a local JSON/JSONL dataset, reporting exact malformed record indexes.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 and idempotentHint, so the safety profile is covered. The description adds that failures are reported as exact malformed record indexes, which is useful output behavior, but says nothing about handling of mixed-validity files, size limits, or whether it stops at the first error.
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?
A single front-loaded sentence stating action, input, and returned value with no filler. Nothing is redundant with the schema or annotations.
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 format need not be restated, and the description still usefully flags the malformed-index report. However, for a nested parameter at 0% schema coverage and a six-value enum, the description leaves meaningful ambiguity an agent must resolve elsewhere.
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% and the single nested object parameter contains an undocumented enum (conversation_jsonl, preference_jsonl, prompt_jsonl, json, jsonl, prepared). The description's 'JSON/JSONL' hints at two enum values but leaves the other four, and the distinction between 'path' and 'dataset_id', unexplained.
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?
Names a specific verb (validate) and resource (local JSON/JSONL dataset) and adds the distinguishing outcome, malformed record indexes. It is clearly separable from siblings like dataset_prepare or dataset_inspect, though it does not explicitly name them.
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?
Usage is implied by 'local JSON/JSONL dataset' and the schema's 'prepared' type, suggesting pre-flight validity checking. There is no explicit when-to-use or when-not-to-use guidance, and no routing to alternatives such as dataset_inspect or dataset_prepare.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.