list_vector_tables

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

With no annotations, the description carries the full behavioral burden. It discloses the return shape (schema, column, data type), which is genuinely useful, but says nothing about whether it is read-only (implied by 'List'), pagination, cost on large catalogs, or permission requirements. For a zero-parameter listing tool the risk is low, so partial credit is warranted.

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?

One sentence, front-loaded with the verb and the filtering scope, with the return contents trailing. No filler and nothing to trim.

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 exists, so the description must hint at what comes back – it does, listing schema, column, and data type. Combined with zero parameters and a simple read, this is nearly sufficient; only the absence of any pagination/volume note keeps it short of 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate. Baseline 4 applies; the description correctly avoids inventing parameter talk.

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?

States a specific verb (List) and a precisely scoped resource (tables containing pgvector vector/halfvec columns), plus what each row reports (schema, column, data type). That scope distinguishes it from the generic sibling list_tables, though it never names the sibling explicitly.

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?

Usage is implied by the scope: an agent can infer this is the right call when hunting for vector-enabled tables before vector_search or vector_execute. However, it gives no explicit when-to-use versus list_tables/describe_tables and no prerequisites or exclusions.

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