list_tables

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

No annotations are provided, so the description carries the behavioral burden. It clearly communicates that this is a read-only listing operation and mentions the optional filter scope, but it does not disclose return format, ordering, or pagination behavior. For a simple list tool this is adequate but not rich.

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?

A single sentence that front-loads the verb and resource, states the optional filter, and wastes no words. Every element contributes to understanding the tool's function.

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 tool with one optional parameter and no output schema, the description provides enough information to call it correctly. The main gap is the lack of explicit routing versus sibling tools, but this is minor given the simplicity of the operation.

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?

With 0% schema description coverage, the description compensates by explaining that table_type filters results and enumerating the valid categories ('marts, facts, dims'). The word 'optionally' signals that null means unfiltered, though the exact default behavior is not explicitly stated.

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 uses a specific verb ('List'), a clear resource ('Gold Schema'), and a scope ('optionally filtered by type (marts, facts, dims)'). It is clear enough to distinguish from siblings like tables_schema, run_a_query, and get_sample_data, but it does not explicitly name the alternatives.

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?

The description implies the usage context: list tables in Gold Schema, optionally narrowing by table type. However, it provides no explicit guidance on when to choose this tool over tables_schema or run_a_query, nor any exclusions or prerequisites.

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