Data Analytics MCP Toolkit

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Updates the dataset in place' (implying mutation) and 'returns data_id and row count' (output behavior), which covers basic operational traits. However, it lacks details on permissions, error handling, or side effects (e.g., whether the original data is recoverable).

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 highly concise and front-loaded: two sentences that efficiently convey purpose and behavior without waste. Every phrase ('Clean dataset', 'optionally drop NA rows', 'z-score normalize', 'Updates in place', 'returns data_id and row count') earns its place by adding critical information.

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 tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the core operation and output but lacks details on parameter interactions, error cases, or integration with sibling tools. Without annotations or output schema, more context on behavioral constraints would be beneficial.

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. It mentions 'optionally drop NA rows' (mapping to 'drop_na') and 'z-score normalize numeric columns' (mapping to 'normalize'), adding meaning for two parameters. However, it doesn't explain 'data_id' or 'session_id', leaving half the parameters undocumented. The value added is partial.

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: 'Clean dataset: optionally drop NA rows and z-score normalize numeric columns.' It specifies the verb ('clean') and resource ('dataset') with concrete operations. However, it doesn't explicitly differentiate from sibling tools like 'load_data' or 'run_analytics' that might also manipulate datasets.

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 no guidance on when to use this tool versus alternatives. It mentions optional parameters but doesn't indicate scenarios where cleaning is needed, prerequisites, or how it relates to sibling tools like data loading or evaluation tools. The agent must infer usage from the purpose alone.

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