list_tables

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds genuinely useful context beyond them: results are live/current rather than static, entries include column counts, and it warns about a specific taxonomy trap ("loose gemstones live in `gems`, not `jewelry`"). It doesn't discuss ordering or the shape of each row, but the output schema exists to cover that.

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 in the first sentence, followed by routing guidance and examples in descending priority. The category enumeration is somewhat long, but it doubles as evidence of scope and the gems-vs-jewelry caveat earns its place.

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, read-only discovery tool with an output schema, this covers everything an agent needs: what it returns, when to call it, which siblings to route to next, and the main taxonomy pitfall. No gap remains that would cause a misinvocation.

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 correctly implies a no-argument call by framing the tool as a discovery entry point, and adds no misleading input expectations.

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 every auction-data table ALT/FNDATA serves") and immediately adds what each entry carries (live descriptions, column counts). It is unmistakably distinct from siblings like get_table_schema and query_table, which consume the aliases this tool returns.

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?

Explicitly says "Start here whenever a question names a category and you need the right table alias," and names the four downstream tools that consume the output. It also gives the when-not guidance ("Always call this for the current set rather than assuming it") plus concrete trigger examples for discovery-style questions.

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