_mcp_split_dataset_config

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

No annotations are provided, so the description must fully disclose behavior. It states it renders a script from a template but omits critical details like whether it writes to disk, requires network, or has side effects. The output schema exists but is not described.

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 a single concise sentence that directly states the tool's purpose with no superfluous words, earning a high score for efficiency despite lacking detail.

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?

Given 4 parameters with no schema descriptions, no annotations, and many siblings, the description is severely incomplete. It does not explain how the script is rendered, what the output is, or how to interpret parameters, making it inadequate for an agent to use correctly.

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 description provides no explanation for any of the 4 parameters (seed, ratios, stratify, project_id), despite 0% schema description coverage. The description must compensate but does not, leaving the agent to guess parameter meanings.

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 clearly states the verb 'render' and the resource 'a seeded train/val/test split Python script from template', making the purpose specific and distinct from sibling tools which cover different functions like generating synthetic data or creating project structures.

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?

No guidance on when to use this tool versus alternatives, such as when to split a dataset vs. generating a synthetic dataset. It does not mention prerequisites, limitations, or preferred scenarios.

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