run_analysis

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 the tool runs a SHAP explainer, but does not mention whether it is read-only, whether it modifies state, whether a model must be loaded first, or what side effects occur. The sample_size parameter hints at auto-sampling, but the overall behavioral profile is opaque.

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 well-organized with a clear purpose statement, a practical note, and a parameter list. Each section is necessary, particularly because the schema lacks parameter descriptions. It is not excessively verbose and is easy to parse.

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?

Although an output schema exists, the description lacks important context. It does not mention whether a model must be loaded first (sibling load_model suggests so), what the default sampling behavior is, or how the tool behaves when conflicting data inputs are provided. These are critical for correct usage.

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?

The schema description coverage is 0%, so the description's parameter block is essential. It clearly explains each parameter: data_path, data_url, data, and sample_size. The guidance about small CSVs and using data directly in chat adds practical semantics. However, it does not explain mutual exclusivity or precedence when multiple data sources are supplied.

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 opens with 'Run SHAP explainer against a dataset', which is a specific verb and resource. It clearly distinguishes this from sibling tools like get_feature_importance or explain_prediction, which have different purposes. The intent is immediately understandable.

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 choose this tool over sibling tools like explain_prediction or get_interaction. The note about sourcing data (local paths, URLs, inline data) is about how to provide input, not which tool to select. No alternatives or exclusions are mentioned.

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