list_tables

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

With no annotations present, the description carries the behavioral burden well: it mentions internal SQLite tables are omitted and that columns are not invented. This guards against false expectations, though it does not address side effects or access requirements, which are minor for a read-only listing tool.

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?

Each sentence earns its place: purpose, usage timing, exclusions, anti-invention behavior, and return shape. The description is front-loaded and compact.

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?

For a zero-parameter listing tool with an output schema, this description is complete. It tells the agent when to use it, what it will not include, and what the response looks like.

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 has zero parameters, so detailed parameter descriptions are unnecessary. The description usefully documents the output contract instead: a table list of names and purposes.

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 names a specific verb and resource: 'List user tables in shop.db', and clarifies the output is a one-line purpose per table. It also differentiates itself by explicitly targeting user tables rather than internal ones, which separates it from schema/query 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?

'Use this first to discover the real schema' gives a clear and explicit usage signal. It does not, however, explicitly state when it should not be used or name alternatives like describe_table or execute_readonly_sql.

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