List Database Tables

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the core safety and variability are covered. The description adds value beyond annotations by explaining the filtering semantics (SQL LIKE pattern) and the fact that omitting the pattern lists all tables, which is useful behavioral context. It does not contradict 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 three sentences, each serving a distinct purpose: the main action (list tables with optional pattern), an example of the pattern, and the routing to two sibling tools. There is no fluff or redundant content, and the key information is front-loaded.

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 tool has an output schema, so return values are documented elsewhere. The description covers the primary use case, the filter behavior, and explicitly directs to alternatives when a different intent is present. The remaining parameters are well-documented in the schema, and the annotation set provides the safety profile. Nothing an agent needs to call this tool correctly is missing.

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?

All three parameters (pattern, profile, magentoRoot) are fully described in the input schema (coverage 100%), so the description need not repeat them. The description's mention of the LIKE pattern matches the schema's description, adding no new meaning. A baseline of 3 is appropriate given the schema carries the parameter documentation.

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 states a specific verb ('Lists') and resource ('Magento database tables'), includes an optional filter example ('sales_%'), and explicitly differentiates from sibling tools by naming describe_table and search_columns as the right choices for column details or column-to-table lookup. An agent can immediately tell this tool apart from related list/search 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?

It gives explicit when-to-use guidance: 'Use describe_table for column details. Use search_columns when you know a column name but not the table.' This directly tells the agent when to select an alternative, and the optional filter clarifies a legitimate use case for this tool. No guesswork is required.

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