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generate_and_post

Generates a post about a specified topic using an LLM and posts it to X (Twitter) with draft approval enabled for human review.

Instructions

Generate a post about topic with the configured LLM and post it. In draft mode (default) the generated text is stored in the draft's payload/preview so a human reviews the actual content before anything is posted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
accountYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description adds significant behavioral context beyond annotations by explaining the draft mechanism: the generated text is stored in a draft's payload/preview for human review before posting. However, it does not disclose whether a non-draft mode exists or other side effects like rate limits or authentication needs.

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?

The description is concise with two sentences. The first sentence clearly states the core functionality, and the second adds the critical draft mode detail. No unnecessary words.

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

Completeness3/5

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

While an output schema exists (reducing the need to explain return values), the description omits important lifecycle context: it does not mention that the tool creates a draft that requires manual approval via sibling tools like 'approve_draft'. The name 'generate_and_post' is misleading as the default behavior is to only draft.

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

Parameters3/5

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

The description explains the 'topic' parameter by name and context, but does not explain the 'account' parameter. With 0% schema description coverage, the description adds partial value but falls short of fully compensating for the gap.

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?

The description clearly states the tool generates a post on a given topic using an LLM and then posts it, with a default draft mode. This distinguishes it from sibling tools like 'post_tweet' (post without generation) and 'draft_post_variations' (generate drafts without posting).

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

Usage Guidelines3/5

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

The description explains the default draft mode but does not explicitly state when to use this tool vs. alternatives such as 'post_tweet' or 'draft_post_variations'. There is no guidance on when not to use it or what the non-draft mode (if any) entails.

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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