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

Annotations only declare readOnlyHint/openWorldHint/destructiveHint, so the description carries the burden — and it delivers richly. It discloses the four refusal guardrails (too early, unmeasured, no baseline, nothing wrong), the platform data limitations (Facebook reach cutoff, Reddit no impressions, Google Business nothing per-post), the crediting/attribution behavior, and the 'nothing here needs fixing' honesty clause. This exceeds what any annotation could convey.

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?

Every sentence carries information — the problem taxonomy, the four refusals, the platform quirks, the converting rule, the 'print summary verbatim' output contract. However, it is a dense wall of all-caps prose; the four refusals are inlined as a numbered run rather than cleanly structured, and the opening hook ('WHAT TO FIX NEXT, post by post — and the tool that fixes it') is somewhat muddled as a lead-in. It earns its length but not its formatting.

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 tool with this diagnostic complexity — three problem rungs, four refusal cases, cross-platform data quirks, and a conditional offer path — the description is remarkably complete. It covers the decision logic, the input conditions, the output contract (print summary verbatim, attribution in output), and credit cost. With no output schema present, the summary-verbatim instruction partially compensates; a slightly more explicit description of the output shape would push this to 5.

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 coverage is 100% and each parameter's schema description is already good. But the tool description adds genuine value beyond the schema: it explains the converting:false trigger condition (only when the user explicitly states non-conversion), and clarifies that limit only caps diagnosis count while 'the baseline is always built from EVERY post recorded.' This reinforces and extends the schema rather than merely restating it.

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 states a specific verb+resource ('tells you what is WRONG with a given post and where the next edit goes') and explicitly contrasts itself with post_performance, which 'tells you which hook is AHEAD.' This gives the agent a precise mental model of the tool's job and how it differs from its closest sibling without opening either schema.

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 opening frames the tool against post_performance ('this tells you what is WRONG...'), establishing a clear when-to-use context. It also states an explicit conditional: the offer rung 'runs ONLY when the user tells you they are not converting and you pass converting:false.' It doesn't enumerate every sibling exclusion, but the named differentiation and the conditional trigger provide solid guidance for a diagnostic tool.

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