Data Analytics MCP Toolkit

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

No annotations are provided, so the description carries the full burden. It mentions the return values (train_data_id and test_data_id) but doesn't disclose critical behavioral traits: whether this is a destructive operation (e.g., modifies original data), authentication needs, rate limits, or error conditions. For a tool with 5 parameters and no annotations, this is a significant gap.

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 extremely concise and front-loaded: two sentences with zero waste. The first sentence states the core purpose, and the second explains the output's utility. Every word earns its place, making it easy to scan and understand quickly.

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 the complexity (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain parameter meanings, behavioral details (e.g., randomness, session handling), or output structure beyond IDs. For a data-splitting tool in a machine learning context, more guidance on usage, assumptions, and limitations is needed.

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 for undocumented parameters. It adds no information about parameters like data_id, target_column, test_ratio, session_id, or random_state. The mention of 'split dataset' implies data_id and possibly test_ratio, but doesn't explain their semantics, formats, or constraints beyond what the schema titles provide.

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 tool's purpose: 'Split dataset into train and test.' It specifies the action (split) and resource (dataset), distinguishing it from siblings like load_data or train_* tools. However, it doesn't explicitly differentiate from run_analytics or other data processing tools beyond the split operation.

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 provides implied usage guidance by mentioning the output is 'for use in train_* and evaluate_* tools,' suggesting this is a preprocessing step. However, it lacks explicit when-to-use rules, alternatives (e.g., cross-validation), or exclusions (e.g., when data is too small). The context is clear but not comprehensive.

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