Data Analytics MCP Toolkit

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'fits' a model and returns a model_id, implying it performs computation and creates a persistent resource, but lacks details on computational requirements, error conditions, side effects, or what 'fitting' entails operationally. This is inadequate for a modeling tool with zero annotation coverage.

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 clear sentences that efficiently state the action and output. Every word earns its place, with no redundant or vague phrasing, making it easy to parse 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?

For a machine learning training tool with 4 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks essential context such as parameter explanations, behavioral traits, error handling, and performance characteristics, leaving significant gaps for effective tool 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 by explaining parameters, but it provides no parameter information. The four parameters (data_id, n_clusters, feature_columns, session_id) are completely undocumented in the description, leaving their purposes and usage unclear beyond what minimal titles in the schema suggest.

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: 'Fit K-means clustering' specifies the verb and algorithm, and 'Returns model_id for evaluate_clustering' indicates the output and downstream use. However, it doesn't explicitly differentiate from sibling training tools like train_linear_regression or train_logistic_regression, which prevents a perfect score.

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 minimal guidance by mentioning the output is for evaluate_clustering, but offers no explicit when-to-use criteria, prerequisites, or comparisons to alternatives. With multiple sibling training tools available, there's no guidance on when to choose K-means over other algorithms or methods.

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