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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds valuable behavioral context: what each entry surfaces (source tool, query parameters, timestamps, row count, column schema) and a prerequisite (CANVAS_PROVIDER_TYPE=duckdb). This goes beyond the annotations and enriches the agent's understanding of the tool's behavior without contradiction.

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 tight sentences, each earning its place: the action/resource, the entry contents, the usage directive, and the environment prerequisite. The critical usage guidance is front-loaded, and there is no redundant or filler language.

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 listing/discovery tool with an optional parameter and an existing output schema, the description covers all operational essentials: what it lists, how to narrow to a single dataframe, the metadata available, the correct usage sequence, and the required environment. Nothing an agent needs to invoke it correctly 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 input schema has 100% schema description coverage, fully documenting the optional 'name' parameter with its format (df_XXXXX_XXXXX) and behavior (describe single vs. list all). The description adds no extra semantic beyond what the schema already states, 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 uses a specific verb ('List') and resource ('DataCanvas dataframes') and ties them to the exact source tools that materialize them. It also differentiates from the sibling tool treasury_dataframe_query by stating its intended role as a discovery step, making the purpose unmistakable.

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 states when to use: 'Use this tool before treasury_dataframe_query to discover table names and column types.' It implies the alternative (treasury_dataframe_query) and provides a clear sequencing directive, leaving no ambiguity about its place in the workflow.

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