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 that the tool 'Returns model_id for evaluate_classification,' which hints at output behavior, but fails to disclose critical traits such as whether it modifies data, requires specific data formats, has computational costs, or error conditions. This leaves significant gaps in understanding how the tool behaves beyond basic functionality.

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 with two sentences that directly state the tool's purpose and output. There is no wasted language, and it is front-loaded with the main action, 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 of a machine learning training tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It lacks details on parameter meanings, behavioral constraints, error handling, and output specifics beyond a model_id, making it incomplete for safe and effective use by an AI 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%, so the description must compensate for undocumented parameters. It adds no information about parameters like train_data_id, target_column, feature_columns, or session_id, leaving their meanings and usage unclear. This results in inadequate guidance for proper tool invocation.

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 action ('Fit a logistic regression classifier') and the resource (a classifier model), distinguishing it from sibling tools like train_kmeans or train_linear_regression. However, it doesn't specify what type of data it works with (e.g., tabular data) or the exact nature of the output beyond returning a model_id.

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 usage for training a classification model, suggesting it should be used when a binary or multi-class classification task is needed, as opposed to regression or clustering tools like train_linear_regression or train_kmeans. However, it lacks explicit guidance on when not to use it or alternatives for similar tasks.

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