silhouette_score

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

With no annotations, the description carries the burden of disclosure. It explains the score range and interpretation thresholds, and gives a usage example. It does not explicitly state that the operation is read-only or mention potential side effects, but for a simple compute tool this is sufficient.

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 four sentences, front-loaded with purpose, followed by interpretation, usage guidance, and an example. Every sentence adds value and there is no wasted wording.

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?

The tool is simple with 3 parameters and an output schema. The description covers purpose, when to use, and interpretation, but parameter semantics are weak, and the description does not explain the df_name parameter or the nature of feature_columns beyond the example. Overall, it is adequate but not complete.

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. It provides an example that shows cluster_column and feature_columns usage, but does not explain what these parameters mean semantically, nor does it mention the optional df_name parameter. The example gives minimal insight but is not adequate.

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 uses a specific verb 'compute' with a clear resource 'silhouette score' and states it evaluates clustering quality. This distinguishes it from sibling tools like kmeans_cluster, dbscan_cluster, and cluster_profile.

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 explicitly states when to use it: 'Run after kmeans_cluster or dbscan_cluster.' It provides clear context for the intended workflow, though it doesn't mention when not to use it or alternative evaluation methods.

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