get_opinion_tables

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

With no annotations, the description carries the full burden of behavioral disclosure. It explains that tables are reconstructed from a geometry-bearing source, notes the two table types, and clarifies that the same content exists inline as a fixed-width block under a '[Table N]' anchor. This gives the agent useful context beyond the basic action, though it does not say what happens for opinions with no tables.

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?

The description is well-structured: the core action is front-loaded in the first sentence, and the following sentences provide useful context about table types and the relationship to plain text without being redundant. It is moderately sized and every sentence contributes to understanding or usage.

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?

The tool is fairly simple with two serializable parameters and an output schema, so the description need not explain return values. It sufficiently covers why tables need reconstruction, when text-cell tables exist, and how the structured output relates to the opinion's text_content, making it complete enough for correct selection and invocation.

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 both parameters. The description adds contextual background but no parameter-level meaning beyond what the schema provides; the format parameter and citation examples are already covered in the input schema.

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 opens with a specific verb and resource: 'Return the reconstructed data tables in an opinion, structured for reading.' It clearly distinguishes this tool from flat-text retrieval tools by emphasizing structured, reconstructed table output and even contrasts it with the opinion's inline fixed-width text_content.

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?

The description explains when this tool is useful: when an opinion contains tables that flat text renders as a scrambled run of cells, and when a cleaner rendering is needed for display or parsing. It references the alternative presentation in the opinion's own text_content, giving implicit guidance to prefer this tool for structured table access, though it does not explicitly name a sibling tool.

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