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

Annotations already signal readOnlyHint=true and idempotentHint=true. The description adds useful behavioral context beyond annotations: it lazy-sweeps expired tables before responding and requires a specific DuckDB environment variable. This goes beyond the annotation baseline 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?

Four short, information-dense sentences with no fluff. The primary action and return contents are front-loaded, followed by usage context, behavioral notes, and environment prerequisites. Every sentence 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 tool with one optional parameter, a full output schema, strong annotations, and clear sibling context, the description is complete. It covers what the tool lists, why and when to use it, a side-effect behavior, and a required environment setting. Nothing important 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?

Schema description coverage is 100%, so the schema already documents the optional 'name' parameter, its expected format (df_XXXXX_XXXXX), and the omit-to-list-all behavior. The description adds no additional parameter-level meaning 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 a specific verb ('List'), a specific resource ('canvas dataframes materialized by bls_get_series'), and the exact metadata returned (provenance, TTL, row count, column schema). This clearly distinguishes it from sibling query tools like bls_dataframe_query and from data-fetching tools like bls_get_series.

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?

Provides explicit usage context: 'Use before writing SQL to confirm column names.' It also notes an environment requirement (CANVAS_PROVIDER_TYPE=duckdb). It does not explicitly name alternatives or state when not to use the tool, but the intended placement before SQL writing is clear enough.

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