List Database Tables

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

No annotations are provided, so the description carries the full burden. It discloses that it reads SQLite directly, makes no LLM call, requires no API key, costs nothing, returns immediately, and returns JSON. This is comprehensive behavioral disclosure beyond what the empty schema provides.

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 moderately lengthy but every sentence adds valuable information: primary output, use-case guidance, and technical behavior. It is front-loaded with the main purpose and remains readable without redundancy.

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 tool with no output schema, this description is entirely sufficient. It covers what the tool returns (table lists, explanations, row counts, columns, relationships, revenue convention), when to use it, and how it executes. Nothing an agent needs to decide to call it 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?

The tool has zero parameters, so the schema requires no explanation. Baseline for 0 params is 4, and the description appropriately does not invent parameter information. No additional semantics needed.

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 a specific verb and resource: lists every table in the e-commerce database, with a plain-language explanation of contents, row count, and column names. It also mentions relationships and revenue convention, distinguishing it from sibling tools like describe_table which likely focuses on a single table.

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 tells the agent to use this first when needing to know what data exists or answer database structure questions. It does not name sibling tools directly or state when not to use it, but the stated context is sufficient for an agent to decide.

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