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

Annotations already mark the tool as read-only, and the description adds meaningful behavioral context: it returns a brief, never generated copy, and explicitly assigns drafting to the caller's AI in the business's voice. This clarifies the tool's boundary beyond the simple readOnlyHint.

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 dense but every sentence earns its place: the first establishes purpose and contents, the second clarifies the output boundary, and the final phrase grounds the division of labor. There is no filler or redundancy.

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 parameterless, read-only tool, the description provides enough detail about what is returned and what the brief contains. Without an output schema, it still communicates the key return semantics and leaves no critical gap for an agent deciding whether to call this 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 tool has zero parameters, so the schema provides complete coverage. The description does not need to elaborate on parameter semantics, and the baseline 4 applies here.

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 names a specific deliverable—a drafting brief for social posts—assembled from measured data, and explicitly distinguishes itself from copy generation. This separates it clearly from sibling get_* research tools, which return raw data views rather than a ready-to-use drafting brief.

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 intended use case is implied: an AI needs this brief when drafting social posts to improve AI visibility. However, the description does not explicitly state when not to use it or name sibling tools as alternatives, relying on the reader to infer context.

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