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

With no annotations provided, the description fully discloses behavior: returns a draft queue + signed reviewUrl + agentGuide block, each action lands in awaiting_approval, and 'nothing fires without explicit human approval.' Also explains the grounding sources (runId, brand voice, repo facts, channel performance). This is comprehensive behavioral transparency for a mutation tool.

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 relatively long but well-structured: trigger condition, function overview, grounding details, and output/approval disclosure. Each sentence provides unique value, and the front-loaded 'USE WHEN' makes the purpose immediately clear. Minor redundancy with schema (e.g., horizon default) but overall efficient for the tool's 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?

For a 4-parameter tool with no output schema and no annotations, the description is exceptionally complete. It covers inputs (runId), process (grounding in company brain), outputs (draft queue, reviewUrl, agentGuide block), safety (requires human approval), and the multi-day planning context. No critical aspects are missing for an agent to invoke and understand the tool's behavior.

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% with descriptions for all four parameters. The description adds semantic enrichment by mapping horizon values to concrete content (day 2 reply post, day 3 case study, day 5 metrics share, day 7 retrospective), which clarifies what planning horizons mean in practice. While the schema already defines defaults, the description's context elevates 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 clearly states the tool's function: 'turns one launch into a 7-day launch mode: drafts a queue of follow-up actions' with specific examples (day 2 reply post, day 3 case study, etc.). It also distinguishes from siblings by noting it grounds in prior recommendation 'so the next move doesn't repeat the last one' and uses the alias prefix.

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?

Provides explicit trigger conditions: 'USE WHEN the user has just published a launch (or ≥24h post-launch) and asks "what's next?"...' This gives clear context for when to use. It does not explicitly mention alternatives or when not to use, but the post-launch condition and the queue-building purpose make it distinguishable from sibling tools like suggest_next_move.

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