Detect preprocessing or resampling steps that occur before the train/test split in your training code, preventing data leakage and invalid evaluation.
Statically detect preprocessing or resampling before the train/test split.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
| 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?
With no annotations, the description carries the full burden. It discloses that the check is static ('statically detect') and that it targets preprocessing/resampling placement, but it does not describe side effects, failure modes, or what happens when no issues are found. The output schema covers some return expectations.
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 focused sentence with no filler. The verb and object are front-loaded and every word adds meaning.
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?
The description is adequate for a one-parameter tool with an output schema, but it omits path semantics and usage routing to dataset-focused siblings. It is not as complete as it could be for a first-time agent.
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 description never explains the 'path' parameter. It does not clarify whether path points to a file, directory, or specific language, so the agent must infer from the tool name.
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 names a specific action ('statically detect') and a specific target ('preprocessing or resampling before the train/test split'). This clearly differentiates it from sibling dataset-focused tools like audit_dataset or profile_dataset.
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 implies the tool is for auditing training code for leakage before splitting, but it does not explicitly state when to prefer it over siblings or mention exclusions. No alternative tools are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.