list_tables

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

No annotations are provided, so the description carries the burden. It adds a behavioral detail ('Uses INFORMATION_SCHEMA for efficiency') and specifies output includes schemas. However, it does not disclose permissions, cost, pagination, or exact return format. For a simple read-only listing 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?

Three sentences, each earning its place: the primary function, an efficiency note, and a usage recommendation. No redundancy, front-loaded with the action.

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 simple 2-parameter read-only tool with no output schema, the description covers the essential aspects: what it does, how it works internally, and when to use it. It does not detail return values, but the purpose implies a list of tables with schemas, making this adequately complete.

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 100% with both parameters documented. The description does not add parameter-level detail, but the schema already provides complete semantics for dataset and project_id, warranting the baseline score.

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 the action: 'List all tables in a BigQuery dataset with their schemas.' It uses a specific verb and resource, and distinguishes from siblings like list_datasets and describe_table by focusing on tables with schemas.

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 provides clear usage context: 'Use this to understand what tables and columns are available before writing queries.' It does not explicitly mention alternatives or exclusions, but the context implicitly differentiates from query tools and describe_table.

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