dbscan_cluster

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

Since no annotations are provided, the description carries the burden of behavioral disclosure. It mentions that features are auto-standardized and that outliers are labeled as -1, which are important behavioral traits. However, it does not explicitly state the output format (e.g., whether a new column is added), though an output schema exists to cover that.

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 concise and well-structured: it starts with the algorithm name, explains its properties, gives usage guidance, and provides an example. Every sentence adds value without unnecessary repetition.

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 moderate complexity, the description covers the essential algorithm characteristics, usage context, and key behaviors, while an output schema exists to document return values. Minor gaps remain for parameter details, but the description is sufficiently complete for an agent to select and invoke this tool correctly.

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 provides no descriptions for any parameters (0% coverage), so the description must compensate. The example 'dbscan_cluster(columns=["Revenue","Weight"], eps=0.5, min_samples=5)' gives concrete context for three parameters, but df_name and cluster_column remain unexplained. The parameter names are somewhat self-explanatory, but the description adds only partial semantic value.

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 identifies the tool as DBSCAN clustering, a density-based method that finds arbitrarily shaped clusters. It explicitly distinguishes itself from k-means by noting that it does not require specifying n_clusters, which separates it from sibling tools like kmeans_cluster.

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 explicit guidance on when to use this tool: 'Use when clusters have irregular shapes or when you need outlier detection.' This gives clear context for selection, and the example invocation reinforces the usage pattern.

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