twilize

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior, including the pipeline steps (schema inference, chart suggestion, etc.), data handling (password used only for schema inference, not stored), and output (.twb file). However, it lacks details on error handling, performance, or rate limits, which are important for a complex tool.

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 and front-loaded with the core purpose, followed by details. Most sentences earn their place, but it could be slightly more concise by integrating the pipeline list into the initial sentence. Overall, it's efficient for a complex tool with many parameters.

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 tool's complexity, 12 parameters, no annotations, and an output schema present, the description is complete enough. It covers the purpose, pipeline, prerequisites, parameter semantics, and return summary, leveraging the output schema for return values. No critical gaps remain for effective tool use.

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?

Given 0% schema description coverage and 12 parameters, the description compensates fully by explaining each parameter's purpose and defaults (e.g., 'password used for schema inference only; not stored in the workbook', 'port default 3306', 'max_charts: 0 = use rules default'). This adds significant meaning beyond the bare schema, making parameter usage clear.

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's purpose with specific verbs ('Build a Tableau dashboard from a MySQL table') and resources ('MySQL', 'Tableau dashboard'), distinguishing it from sibling tools like csv_to_dashboard or hyper_to_dashboard by specifying the MySQL source. It outlines an end-to-end pipeline, making the purpose explicit and distinct.

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 context for when to use this tool (e.g., for building dashboards from MySQL tables) and mentions prerequisites ('Requires mysql-connector-python for schema inference'), but it does not explicitly state when not to use it or name alternatives like mssql_to_dashboard for other data sources, leaving some guidance implicit.

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