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

Annotations already declare readOnlyHint and idempotentHint, but the description adds valuable context beyond those: it clarifies that the row count reflects what was actually staged and instructs checking truncated in the prior response to know if the canvas holds the full set. This is meaningful behavioral disclosure that helps the agent interpret results correctly, going beyond the boolean hints.

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?

Each sentence serves a distinct purpose: what the tool does, when to use it, and an important caveat about row counts. The information is front-loaded and compact, with zero filler. It's an optimal length for quick agent comprehension.

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 discovery tool that feeds a query tool, the description covers the essential context: the source of canvas_id, the need for prior staging via fema_search_nfip, the purpose of discovering schemas, and a pitfall (truncation). Since an output schema exists, the description doesn't need to detail return values. Nothing essential 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 only parameter canvas_id is fully described in the input schema ('Canvas ID from the fema_search_nfip response'), and schema description coverage is 100%. The description does not add extra semantic detail beyond what the schema provides, 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 states the primary action ('List tables and column schemas'), identifies the resource (DataCanvas staged by fema_search_nfip), and explicitly differentiates from the sibling fema_dataframe_query by positioning itself as a prerequisite. This goes beyond a generic 'describe table' and gives the agent an actionable purpose.

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?

It provides explicit when-to-use guidance: 'Call this before fema_dataframe_query to discover the exact table name, column names, and DuckDB data types needed to write valid SQL.' It also advises checking the 'truncated' field in the fema_search_nfip response, giving the agent a clear decision point. This is strong usage direction that directly influences tool selection order.

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