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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds meaningful behavioral detail: it returns row counts and column schemas for tables staged by a previous call, and it exposes DuckDB types. There is no contradiction with the annotations.

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 dense sentences with no filler. The action and scope are front-loaded, and the 'Required before imf_dataframe_query' clause gives essential workflow context 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 one-parameter, read-only, idempotent introspection tool with a rich input schema and an output schema present, the description fully equips an agent to select and invoke it correctly. Nothing critical 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 schema provides 100% coverage of the single canvas_id parameter, including its origin conditions (staged=true, automatic spillover, output_mode="canvas"). The description reinforces that context but does not add new parameter-level meaning, so the high-coverage 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?

States a specific verb ('List') and resource ('DataCanvas tables and columns staged by a prior imf_query_dataset call'), then specifies the return content: table name, row count, and column schema with DuckDB types. It also distinguishes itself from imf_dataframe_query by framing this as the discovery step needed before querying.

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?

Clearly defines when to use it: after imf_query_dataset and before imf_dataframe_query, since it is required to discover table/column names for SQL. It does not explicitly list exclusions for other sibling tools like imf_list_databases, but the sequencing and prerequisite context are strong.

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