Cluster vectors

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

The description discloses algorithm details (Lloyd's, k-means++ seeding), metric behavior (l2 vs cosine normalization), determinism via seed, sampling, and output structure (centroids with size, assignments with cluster index and distance). Annotations (readOnlyHint: true) are consistent with the read-only nature of clustering. No contradictions.

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 yet comprehensive, using a single paragraph with an illustrative example. Every sentence adds value: start with verb, then algorithm details, then parameter specifics, then a clear example. No redundant or vague statements.

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 10 parameters, 4 required, and the presence of an output schema (which handles return values), the description covers all necessary aspects: purpose, conditions, algorithm details, parameter behavior. The example ties everything together. It is complete for an AI agent to understand and invoke the 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?

Schema coverage is only 10% (only 'database' parameter has a description), but the description compensates fully by explaining the role and default of sample_size, seed, metric, id_column, k, and max_iterations. The example further clarifies parameter usage. This adds significant meaning beyond the bare schema.

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 performs k-means clustering on a pgvector column, specifying the algorithm, inputs, and outputs. It distinguishes itself from sibling tools by being a dedicated clustering function, which is unique among the listed 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?

The description provides explicit usage context: requires pgvector installed, k >= 2, at least 2k rows, and default sample size. It also mentions fallback behavior ('Reports available=false if pgvector is not installed'). Although no direct alternatives are mentioned, the tool's purpose is distinct, making usage clear.

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