Vector search

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

Annotations declare readOnlyHint=true and openWorldHint=false, which the description aligns with by describing a read-only search. It additionally discloses behavior when pgvector is not installed ('available=false'), adding useful context beyond annotations.

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 with two sentences and an example, front-loading the core purpose. Every sentence adds value, and the example provides immediate clarity.

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?

The description covers purpose, metrics, availability check, and example usage. Given the tool complexity (7 parameters) and presence of an output schema, it is fairly complete but lacks usage guidelines against siblings.

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 only 14%, but the description compensates partially with an example showing all key parameters. However, it does not individually describe each parameter's meaning or constraints, leaving some gaps.

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 finds rows nearest to a query vector using pgvector distance metrics (l2, cosine, inner_product). It distinguishes from other search tools by specifying vector search and reporting extension availability.

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 implies usage for vector similarity search but provides no explicit guidance on when to use this tool versus siblings like hybrid_search or fuzzy_search. No exclusion criteria or context for selecting this tool.

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