list_tables

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

Annotations already declare readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context: that the call is free and scans no data — a cost/side-effect trait not present in the annotations. It does not describe result shape or pagination.

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?

Purpose and the key 'free/no scan' fact are front-loaded in the first two sentences, and the args block is tight with no filler. Every line 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 two-parameter, read-only list tool with no output schema, the definition covers purpose, cost, and both parameters adequately. The only minor gap is not stating what the returned entries contain (e.g., names only vs. metadata), but this is not critical for invoking it correctly.

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?

Schema description coverage is 0%, so the description carries the burden and largely meets it: it explains dataset_id (with a concrete example) and environment (default behavior plus a pointer to list_environments). Both parameters get meaning beyond their bare names and types.

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 with scope: 'List tables and views inside a dataset.' It's clear what the tool returns at a high level, though it doesn't explicitly differentiate itself from siblings like list_datasets or get_table_schema beyond the word 'inside a dataset'.

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?

Gives a cost-based usage signal ('Free — scans no data') and routes the agent to a sibling for the environment parameter ('Call list_environments to see what exists'). No explicit when-not-to-use guidance, but the selection context is clear.

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