get_dashboard_sql_from_url

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

No annotations provided, so the description carries full burden. It describes the output as a 'complete breakdown' including dashboard info, sheets, datasources, and custom SQL. However, it does not disclose potential limitations, error conditions, or authorization requirements.

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?

Concise, front-loaded with key purpose, then provides usage context, parameter description, and return summary in a well-structured manner. Every sentence serves a clear purpose.

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?

Given the single parameter, presence of an output schema (implied by 'Returns'), and sibling tool list, the description sufficiently covers what the tool does, what it takes, and what it returns. Missing details on error handling or permissions, but adequate overall.

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 0%, but the description adds significant value by defining the 'url' parameter as a 'Full Tableau dashboard URL' with an example. This clarifies the expected format beyond the basic type string.

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?

Description clearly states the tool retrieves all custom SQL queries for a Tableau dashboard from its URL, with explicit mention of being the 'MAIN tool for reverse engineering'. This distinguishes it from sibling tools like get_datasource or get_view_data.

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 a clear use case question ('What SQL/tables power this Tableau dashboard?') and positions itself as the main reverse engineering tool. Lacks explicit when-not-to-use or alternative tool mentions, but the usage context is strong.

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