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Generate post with AI

generate-post

Generate a new post using AI based on a prompt and save it as a draft, ready for update-post, schedule-post or publish-post. Uses 1 AI credit (Lite mode with a daily cap when credits run out). Returns the generated post.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "generate-post"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYesWhat the post should be about
isThreadNoGenerate a thread instead of a single post (X only; ignored for LinkedIn)
platformNoTarget platform: "x" (default), "linkedin", "bluesky", "mastodon", "youtube", "instagram" or "tiktok"
profileIdNoVoice profile ID to use (omit to use default brand voice)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / platform / description
      Previous value: -"Target platform: \"x\" (default), \"linkedin\", \"bluesky\", \"mastodon\", \"youtube\" or \"tiktok\""New value: +"Target platform: \"x\" (default), \"linkedin\", \"bluesky\", \"mastodon\", \"youtube\", \"instagram\" or \"tiktok\""
  2. Changed1 schema field changed
    • changedInput schema / properties / platform / description
      Previous value: -"Target platform: \"x\" (default), \"linkedin\", \"bluesky\" or \"mastodon\""New value: +"Target platform: \"x\" (default), \"linkedin\", \"bluesky\", \"mastodon\", \"youtube\" or \"tiktok\""
  3. Changed1 schema field changed
    • changedInput schema / properties / platform / enum
      Previous value: -[
      -  "x",
      -  "linkedin",
      -  "bluesky",
      -  "mastodon"
      -]New value: +[
      +  "x",
      +  "linkedin",
      +  "linkedin_page",
      +  "bluesky",
      +  "mastodon",
      +  "threads",
      +  "youtube",
      +  "instagram",
      +  "tiktok"
      +]
  4. Changed2 schema fields changed
    • changedInput schema / properties / platform / description
      Previous value: -"Target platform: \"x\" (default) or \"linkedin\""New value: +"Target platform: \"x\" (default), \"linkedin\", \"bluesky\" or \"mastodon\""
    • changedInput schema / properties / platform / enum
      Previous value: -[
      -  "x",
      -  "linkedin"
      -]New value: +[
      +  "x",
      +  "linkedin",
      +  "bluesky",
      +  "mastodon"
      +]
  5. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations only say readOnlyHint=false, destructiveHint=false and openWorldHint=true; the description adds genuinely non-obvious behavior — the call consumes 1 AI credit and silently degrades to 'Lite mode with a daily cap when credits run out'. It also confirms the artifact is persisted as a draft, not published, and that the generated post is returned. What is missing is failure behavior (what happens if credits are exhausted entirely, or if generation fails mid-call) and whether a draft is scoped per platform/profile.

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?

Three short sentences, front-loaded with the action and artifact, then cost/rate behavior, then the return value. Every sentence carries distinct information and nothing is restated from the title.

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

Completeness4/5

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

For a four-parameter generation tool with no output schema, the description covers action, persistence state, cost, fallback mode and return value — enough to call it correctly. It is slightly thin on the credit-exhaustion edge case and on how the draft relates to platform/profile, which are the only realistic gaps.

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?

Schema description coverage is 100%, so all four parameters (prompt, isThread, platform, profileId) are already documented in structured fields and the baseline is 3. The description adds no parameter-level meaning — notably it never mentions thread generation or the platform/voice-profile choices, so an agent must read the schema to discover that isThread is X-only.

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 names a specific verb and resource ('Generate a new post using AI based on a prompt') and adds the crucial side effect that the result is saved as a draft rather than published. That distinguishes it from the sibling write tools (create-post, publish-post, schedule-post) without the agent needing to open another schema.

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?

It clearly places the tool in a workflow: output is 'ready for update-post, schedule-post or publish-post', which tells the agent this is the entry point and those siblings are the follow-ups. It does not state when NOT to use it (e.g. hand-writing content via create-post, or regenerating an existing draft via refine-post), so the routing guidance is clear but not exhaustive.

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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