cluster_documents_dbscan

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

No annotations exist. The description discloses that the tool discovers clusters and identifies outliers, and that it's density-based. It does not mention any side effects, performance, or prerequisites for using the tool.

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?

Two sentences, no fluff. Every word adds value. Very concise and easy to scan.

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?

No output schema is provided, and the description does not explain what the tool returns (e.g., cluster assignments, outlier flags). Given the complexity of clustering, more detail on output would be helpful. However, it covers the algorithmic behavior adequately.

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?

Input schema has 100% coverage with descriptions for eps and min_samples. The description adds algorithmic context but no additional meaning beyond schema. Baseline 3 is appropriate.

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 it clusters documents using DBSCAN, mentions density-based, automatic cluster discovery, and outlier identification. It distinguishes from sibling like cluster_documents_kmeans by noting it doesn't require specifying number of clusters.

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 provides a key differentiator (no need to specify number of clusters) which implies when to use over kmeans. However, it doesn't explicitly state when not to use or mention alternatives like HDBSCAN, though sibling list includes multiple clustering methods.

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