Write one platform-native post from an idea, grounded in the Brain (the workspace's, unless brain_id names another). Draws one AI generation.
| Name | Required | Description | Default |
|---|---|---|---|
| idea | Yes | ||
| brain_id | No | ||
| platform | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does disclose that the tool draws one AI generation and grounds the post in a Brain, which are useful behavioral traits. But it does not say whether the post is saved, published, pending approval, or what permissions or irreversible effects are involved.
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 two compact sentences with no filler. It front-loads the core action, then adds context about grounding and the AI generation cost, so every sentence earns its place.
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?
Without annotations or an output schema, the description leaves important invocation context unspecified: what the tool returns, whether the composed post is immediately published or merely drafted, and how the platform parameter behaves. For a state-changing generative tool, this is a meaningful completeness gap.
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 0%, so the description must compensate. It explains 'idea' as the source of the post and clarifies that 'brain_id' overrides the default workspace Brain. The 'platform-native' wording hints at the platform parameter but does not explain possible values or how the platform is selected, leaving partial parameter coverage.
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 a specific action ('Write one platform-native post from an idea') and resource (a post), and adds meaningful context about being grounded in the Brain. It does not explicitly contrast itself with siblings like publish_now or schedule_post, so it stops short of full sibling differentiation.
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 implies when to use the tool: when the user has an idea and wants an AI-generated post grounded in a Brain. However, it provides no explicit when-not-to-use guidance and does not name alternatives, so the usage context is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct action or resource: plan approval, scheduling, publishing, analytics, planning, and listing all have clear boundaries. Even the schedule/publish/approve tools are easy to distinguish because their source objects and outcomes are explicitly different.
The naming almost uniformly follows a verb_noun snake_case pattern, such as list_brains, compose_post, and cancel_scheduled_post. The one small deviation is publish_now, which uses an adverb rather than a noun object, but the overall pattern remains highly predictable.
14 tools is well within the ideal range for a content planning and social media scheduling server. Each tool addresses a meaningful part of the workflow without unnecessary bloat or redundant helpers.
The tool surface covers planning, composing, scheduling, publishing, canceling, analytics, and workspace/channel context. However, there are notable lifecycle gaps: no update or delete operation for drafts or scheduled posts, and no way to fetch a single post's detail beyond a list.