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

The annotations provide no behavioral details (readOnlyHint=false, destructiveHint=false, etc.), so the description carries full burden. It excels: discloses the pipeline (VoiceDNA, past posts, substance bank), latency (30-90 seconds), side effects (saves draft to account), and the specific needs_input protocol. It warns that paraphrasing upstream destroys voice fidelity. No contradictions with annotations.

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 front-loaded with the critical purpose ('THE MAIN TOOL') and structured logically: purpose, when-to-use, how-it-works, param guidance, workflow integration, latency, error handling. Every sentence earns its place. Could trim 'their own words, their own details, their own numbers' (slightly redundant) but overall very tight for the complexity it covers.

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?

Given no output schema, the description compensates by explaining return behavior (post_id for scheduling/publishing, needs_input protocol). With 3 parameters and 7 sibling tools, it clearly differentiates itself (main tool vs repurpose_post, generate_ideas, etc.). It covers latency, side effects, error states, and constraints — nothing is missing for safe agent invocation.

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 all three parameters are described in the schema. The description goes well beyond schema by explaining the 'why' and 'how' for user_idea (verbatim is better than tidy, include full intent/context/names/numbers), providing inference guidance for style enum (results=proof, educational=authority, viral=growth), and clarifying user_constraints as override mechanism for voice defaults. This adds significant operational meaning.

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 this is the main tool for writing a LinkedIn post in the user's own voice using their material. It specifies the exact use case ('whenever the user describes something they want to post about') and distinguishes itself from siblings by naming the voice pipeline, draft-saving behavior, and the specific workflow integration (can pass post_id to schedule_post or publish_post).

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 provides explicit when-to-use guidance ('whenever the user describes something they want to post about — a story, an opinion, a result, a lesson, a rough brief') and when-not-to-use ('Do NOT tidy it up, summarise it, or replace their phrasing'). It also tells the agent exactly what to do if needs_input: true is returned ('relay that question to the user... Do NOT write, invent, or promise a post, and do NOT call this or any other write tool again until the user answers'). This prevents looping and hallucination.

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