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 mentions the output (model_id) but fails to describe critical behaviors: whether training is resource-intensive, if it modifies existing data, what happens on errors, or any performance characteristics. This leaves significant gaps for an AI agent to understand the tool's operational impact.

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 brief and front-loaded with the core action, consisting of two concise sentences. However, the second sentence about evaluate_regression feels slightly tacked on without integration into a broader workflow explanation, slightly reducing efficiency.

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, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It lacks details on input formats, error handling, performance, and integration with sibling tools, making it incomplete for reliable agent use.

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 but adds no parameter semantics. It doesn't explain what train_data_id refers to, how target_column and feature_columns should be formatted, or the purpose of session_id. This leaves all 4 parameters undocumented beyond their schema types, creating ambiguity for proper usage.

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 linear regression model') and the resource ('model'), distinguishing it from siblings like train_kmeans or train_logistic_regression. However, it doesn't specify what 'fit' entails beyond returning a model_id, leaving some ambiguity about the training process itself.

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 like train_logistic_regression or train_kmeans, nor does it mention prerequisites such as needing pre-loaded data via load_data. It only hints at a follow-up action (evaluate_regression) without explaining the context for choosing linear regression over other methods.

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