get_favorites

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

With no annotations provided, the description carries the burden of behavioral disclosure. It mentions that the tool returns workbook information including URLs, which gives some transparency about output. However, it does not disclose limits, pagination, sorting, or error conditions, which would be valuable for a complete picture.

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 three sentences and front-loaded with the main action. It is concise and readable, with the second and third sentences adding context about the return content and use case. Slightly redundant phrasing ('valuable or interesting') could be tightened, but overall it is well-structured.

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 one-parameter read tool with no output schema or annotations, the description covers the basic purpose and return contents, but omits important practical details like pagination, empty-list behavior, or any authentication assumptions. It is adequate but leaves gaps for a fully autonomous agent.

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%, so the description should compensate. It mentions 'a Tableau Public user' but does not explicitly detail the 'username' parameter, its format, or any constraints. The schema itself only identifies the parameter as a required string, leaving the agent to infer semantics from the tool description.

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 retrieves a list of favorited workbooks for a Tableau Public user, using a specific verb ('Retrieves') and resource ('favorited workbooks'). It distinguishes from siblings by focusing on favorites, which is not covered by other tools like get_workbooks_list or get_user_profile.

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 a use case ('useful for understanding user preferences and discovering quality visualizations') but does not explicitly state when to use this tool versus alternatives, nor does it give exclusions or prerequisites. This is implied context rather than direct guidance.

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