data_get_pine_tables

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

No annotations are provided, so the description must disclose behavior. It explicitly states 'Read' (non-destructive), mentions the return format ('formatted text rows per table'), and indicates this is data from Pine Script tables. While it doesn't detail error cases or multi-table behavior, it adequately covers the core behavior for a simple read operation.

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 two sentences, front-loaded with the primary action, and every clause adds value—purpose, output format, and filter guidance. No redundant or filler content.

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 simple read-only tool with one optional parameter and no output schema, the description covers the essential aspects: what it does, return type, and filtering. It is complete enough for an agent to invoke correctly without additional clarification.

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%, and the description's 'Use study_filter to target a specific indicator' largely repeats the schema's 'Substring to match study name.' It adds minimal semantic value beyond the schema, so baseline 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 clearly states the tool reads table data drawn by Pine Script indicators (table.new) and returns formatted text rows per table. This distinguishes it from sibling tools like data_get_pine_lines, data_get_pine_labels, and data_get_pine_boxes, which target other drawing types.

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 provides clear usage context by specifying that study_filter targets a specific indicator, and the schema notes 'Omit for all.' It does not explicitly name alternative tools for other data types, but the purpose is self-evident given the tool name and siblings.

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