list_databases

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

No annotations are provided, so the description carries the full burden. It usefully discloses the two 'kind' categories and the alias-based naming pattern for connection backends, but it does not state that this is a pure read with no side effects, nor whether results are paginated or ordered.

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?

Front-loaded with the purpose, followed by two dense clauses that each carry real information. It is tight, though the second clause packs a lot of dotted-name syntax into one sentence.

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?

With no output schema, the description must hint at the return shape; it does so by explaining the 'connection' vs 'local' kinds and the naming format of backend tables. It stops short of describing the actual result structure (e.g., names plus kind fields).

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 the baseline is 4. The description's discussion of 'kind' describes returned data rather than an input, so there is nothing further for it to document on the parameter side.

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 and resource ('List OtterStax databases') and then defines the two distinct kinds of database it can return, which clearly separates it from siblings like list_schemas and list_tables.

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 only implied: an agent can infer this is the entry point for discovering databases before drilling into schemas/tables, but the description never says when to call it versus list_schemas or search_objects, nor any preconditions.

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