list_tables

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

The description implies a read-only listing operation but does not explicitly state the absence of side effects, permissions needed, or error behavior. With no annotations provided, the description carries full burden, yet the operation is simple and self-evident.

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 and well-structured, with the purpose stated first followed by a brief parameter explanation. No unnecessary details are included.

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 the tool's simplicity and the existence of an output schema, the description adequately covers the purpose and parameter behavior. It does not need to explain return values in detail.

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 Args section adds meaning beyond the raw schema: it explains that schema is a database/namespace name, is case-insensitive, and that omitting it uses the default namespace. This fully clarifies the parameter's semantics.

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 action ('List tables') and the resource ('tables in the database'), with a condition for filtering by namespace. It is distinct from sibling tools like execute_query, describe_table, and test_connection.

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?

It provides practical guidance by explaining the effect of specifying schema versus not specifying it, but it does not explicitly mention when to prefer this tool over alternatives or list exclusions.

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