cluster_profile

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

With no annotations provided, the description carries the full burden. It discloses the mean calculation, the default behavior when feature_columns is empty (all numeric columns), and includes an example. This adds meaningful behavioral detail beyond the schema.

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 only two sentences plus an example, with the core purpose front-loaded. Every sentence adds value, and there is no redundancy or fluff.

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 tool's simplicity and the presence of an output schema, the description covers the main behavior well. However, it misses the df_name parameter semantics, which is needed for complete understanding of how the tool is invoked.

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 coverage is 0%, so the description must add meaning. It explains feature_columns (and its default behavior) and gives an example showing cluster_column usage. However, it completely omits df_name, which is a required parameter in practice, leaving a semantic gap.

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 computes descriptive statistics (mean of each feature) per cluster, with a specific verb and resource. It also adds context on usage ('to understand what characterizes each cluster'), distinguishing it from general statistics tools.

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 explicitly states 'Use to understand what characterizes each cluster,' providing a clear when-to-use scenario. It doesn't mention alternatives or exclusions, but the context is sufficient for a specialized profiling tool.

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