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publish_smart_thread

Splits long text into tweets and posts them as a threaded reply chain, enabling publication of content that exceeds the 280-character limit.

Instructions

Splits long AI-generated text into multiple tweets (each ≤280 characters) and posts them as a threaded reply chain. Use this tool when the LLM needs to publish content that exceeds the 280-character single tweet limit—for example, announcements, tutorials, story threads, listicles, or any long-form content. The content is split first by paragraph breaks (double newlines) then by sentence boundaries. Each chunk is posted in sequence as a reply to the previous tweet, forming a connected thread. Returns the full thread with tweet IDs and a URL to the first tweet.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe full text content to publish as a thread. Can be thousands of characters long. The tool automatically splits the content into individual tweets (each ≤280 characters) by paragraph breaks (double newlines) and posts them as a threaded reply chain. Use double newlines to indicate where you want tweet breaks to occur. Supports Unicode, emoji, hashtags, mentions, and URLs.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYesIndicates the outcome of the operation: "success" or "error".
messageYesA human-readable summary of the result.
dataYesContainer holding the posted thread details.
Behavior5/5

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

Describes splitting algorithm (paragraph breaks then sentence boundaries), posting sequence (reply chain), and return (thread with IDs and URL). No annotations to contradict. Full behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with main action, then conditions and examples. Every sentence adds value. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all necessary aspects: when to use, how it works, return value. Output schema exists but description also mentions thread with IDs and URL. No missing information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% with parameter description, but the tool description adds significant context: how splitting works, 280-char limit, double newline usage. Provides meaningful extra guidance beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it splits long text into tweets and posts as a threaded reply chain. Distinguishes from siblings like post_tweet (single tweet) and draft_quote_tweet (drafting).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use: when content exceeds 280-character limit (e.g., announcements, tutorials). Does not explicitly say when not to use, but context implies short content should use post_tweet. Could be improved with explicit exclusion.

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

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