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

Beyond the annotations (idempotent, non-destructive), the description discloses several important behavioral traits: the "honest empty + warning" behavior for zero-matching filters, "no credits charged" on that path, "transcript: null" when captions are disabled/geoblocked, and Instagram's "lifetime totalLikes defaults to 0." This level of edge-case disclosure goes well beyond what annotations provide and helps the agent set accurate user 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 information-dense but well organized: action first, outputs second, usage trigger third, platform-specific behavior last. Every sentence contributes unique value, including the credit-charge warning and platform caveats. Despite its length, it is front-loaded and easy to scan, and the complexity of the tool justifies the length.

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?

The description covers the core action, all supported platforms and their transcript availability, key output fields, usage context, and important edge cases (empty results, credit charges, platform data limitations). An output schema exists, so return values don't need to be re-explained. Nothing essential for selecting or invoking this tool is missing.

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 100%, with each parameter (handle, since, window, platform, recent_only) already thoroughly documented in the schema. The description adds high-level context—like platform transcript expectations and the since vs. window exclusivity—but for the most part it does not need to explain parameters because the schema already carries that burden. Baseline 3 is appropriate.

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: "Analyze a TikTok, YouTube, or Instagram creator by handle or channel ID." It then enumerates a distinctive set of outputs (viral DNA scores, content patterns, headline_insight, etc.) that clearly separates this from sibling tools like watch_account or predict_virality. An agent can confidently identify what this tool does from the first sentence alone.

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 when the user asks to analyze a creator, account, channel, or competitor." This is clear and actionable. However, it does not mention when not to use it or point to an alternative sibling (e.g., watch_account for ongoing monitoring), so it stops short of the full when/when-not/alternatives guidance required for a 5.

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