cluster_documents_hdbscan

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

With no annotations, the description must carry the burden. It notes 'automatically discovers clusters and outliers' but omits details like computational cost, parameter sensitivity, or pre-processing requirements (e.g., need for document embeddings).

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 two effective sentences: first defines the tool, second lists advantages. It is concise without being under-specified.

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 no annotations and no output schema, the description is adequate but lacks critical context such as required input format (e.g., embeddings), output structure, or prerequisites. It covers the core purpose but is not fully 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 100%, so the baseline is 3. The description does not add any additional meaning beyond what the schema already provides for min_cluster_size and min_samples.

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 'Cluster documents using HDBSCAN' and highlights key features (handles varying densities, discovers clusters and outliers), but does not explicitly differentiate from sibling tools like dbscan or kmeans.

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?

No guidance is provided on when to use HDBSCAN versus its alternatives (e.g., DBSCAN, k-means). The description mentions 'advanced clustering' and 'handles varying densities' but does not give concrete scenarios or exclusions.

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