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

Annotations already indicate readOnlyHint=true, so the read-only nature is covered. The description adds detail about the return content (each measurement table with row count and column names) and the DataCanvas requirement, providing context beyond the annotation without contradicting it.

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?

Two sentences with zero waste. The primary action is front-loaded ('List the tables and columns'), followed by purpose, return specifics, and a prerequisite. Every sentence contributes necessary information.

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?

Given the presence of an output schema (as indicated by the context signal), the description need not detail return types. It already explains what the tool returns (tables, row counts, column names) and the prerequisite. An agent receives enough to call it correctly without missing context.

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 coverage is 100% and the canvas_id parameter has a clear, specific description ('DataCanvas id returned by openaq_get_measurements when a series spilled'). The tool description itself does not add parameter details, but the schema fully documents the parameter, so the baseline of 3 is appropriate.

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 verb (list), the resource (tables and columns on a DataCanvas), and the intended purpose (writing valid SQL for openaq_dataframe_query). It distinguishes itself from sibling query tools by focusing on schema discovery rather than data retrieval.

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 explains when to use: 'so you can write valid SQL for openaq_dataframe_query without guessing column names.' It also names the prerequisite ('Requires DataCanvas to be enabled') and implicitly positions it as a setup step before querying. No ambiguity about its role in the workflow.

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