get_moltbook_advisor

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 fully discloses costs, model tiers, and what each tier returns, which is critical for an agent making a paid call. It explains the analysis process (trends, feed data) and the nature of the output (viral score, brief, suggestions, draft). This exceeds the typical minimal disclosure and provides actionable expectations.

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 reasonably concise and front-loads the core purpose, then details tiers, usage, and cost. Each sentence contributes essential information without redundancy. It could be slightly tighter, but the structure is logical and the length is appropriate for the complexity.

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?

Since there is no output schema, the description must explain what the tool returns, and it does: standard returns viral score + brief + suggestions; premium adds deeper analysis and a full draft. It also covers cost and the usage context. This is comprehensive enough for an agent to know what to expect and when to call it. Minor gaps like error handling are not critical for this type of tool.

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?

The input schema has 100% description coverage, so all parameters (prompt, tier, submolt) are already documented. The description adds no new parameter-level meaning beyond what the schema states; it merely reiterates tier costs and capabilities that are already in the enum description. Thus, the description does not compensate or enhance parameter understanding beyond the 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: 'Get AI-powered viral content advice for Moltbook posts.' It then details what the advice includes (score, angles, formats, titles), which makes the tool's function unmistakable. It clearly distinguishes itself from sibling data-retrieval tools like get_moltbook_sample or get_moltbook_digest by focusing on analysis and advice rather than raw 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?

The description explicitly states when to use it: 'Use this before writing a post to maximize engagement.' This gives clear context for the agent. While it does not enumerate alternatives or exclusions, the unique purpose among siblings is evident, and the usage timing is specific enough to guide correct invocation.

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