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

Annotations already declare readOnly=true, destructive=false, and closed-world behavior, so the safety profile is covered. The description adds useful behavioral context by stating that it returns a SHAP contribution plot and that computation is done by a real algorithm engine rather than an LLM. It still does not describe model compatibility, data validation, or output format details beyond 'plot'.

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 compact and front-loaded: it names the tool, gives the trigger, states the return, and adds an execution note. Every sentence contributes useful information without redundant restatement of the title or schema.

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 tool with three optional parameters and no output schema, the description identifies the purpose and return artifact but omits important invocation context. It does not say what model types are supported or how CSV data should be prepared. The annotations cover safety, and the schema covers parameters, but the description leaves some operational gaps.

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 100%, so csv_data, model_type, and sample_index are already documented in the schema. The description does not add meaning about expected CSV structure, supported model types, or sample index semantics. Baseline 3 is appropriate when the schema carries the parameter documentation.

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 states a specific verb and resource: explaining a single sample via SHAP contribution. The phrase '单样本解释' clearly scopes it to one-sample explanation, which separates it conceptually from aggregate tools like PSI. However, it does not explicitly name or differentiate itself from sibling tools such as act_ml_psi or act_ml_nomogram.

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?

It gives a clear usage trigger: when the user wants to explain why a patient was judged high-risk. This is a concrete context, not merely implied usage. It lacks explicit exclusions or named alternatives, which keeps it from a 5.

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