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SpeedContent Social

by io.github.seotrader
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Generate a social media post

generate_social_post

Write a social media post from scratch for a given platform and topic. Use this when you do not already have a draft — for example to produce several variations at once, or for platforms whose conventions you are unsure of. If you have already written the post yourself, prefer score_text and humanize_text, which check and fix your own wording instead of replacing it. Returns a job id immediately — poll it with check_social_job, which usually completes in 20-90 seconds. Costs credits.

Input Schema

NameRequiredDescriptionDefault
toneNoDefaults to friendly.
emojiNoAdd emoji to the post. Omit for automatic: off on LinkedIn and YouTube, on elsewhere. Set false for a clean post on any platform.
topicYesWhat the post should be about.
humanizeNoRewrite the post to read as human-authored. Omit for automatic: the draft is scored first and humanized only if it reads as machine-written. Skipping it keeps the exact wording and saves credits, since a well-written post often already passes.
languageNoOutput language by name. Defaults to English.
platformYesTarget platform. Each has its own style guide and default length.
preserveNoExact lines that must appear verbatim in the finished post — a closing question, a call to action, a tagline. The humanizer rewrites sentences, so anything whose exact wording matters belongs here rather than only in the topic.
quantityNoHow many distinct variations to generate. Defaults to 1.
detect_aiNoScore the post against AI detection and retry if it reads as machine-written. Adds 8 credits per post. Omit for automatic: runs only at 150+ words, because short posts score unreliably.
brand_nameNoBrand to mention where it reads naturally.
brand_toneNoVoice guidance, e.g. 'Direct, no jargon'.
word_countNoTarget length per post. Omit to use the platform default (LinkedIn 100, YouTube 150, Facebook/Pinterest 75, Instagram/TikTok 50, X 40).
brand_audienceNoWho the post should speak to.
brand_keywordsNoComma-separated terms to work in.
generate_imageNoGenerate a matching image for the post. Defaults to false over MCP; pass true to request one. Adds 10 credits per post.
brand_descriptionNoWhat the business does.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / humanize
      Added value: +{
+  "description": "Rewrite the post to read as human-authored. Omit for automatic: the draft is scored first and humanized only if it reads as machine-written. Skipping it keeps the exact wording and saves credits, since a well-written post often already passes.",
+  "type": "boolean"
+}
  2. Changed3 schema fields changed
    • addedInput schema / properties / emoji
      Added value: +{
+  "description": "Add emoji to the post. Omit for automatic: off on LinkedIn and YouTube, on elsewhere. Set false for a clean post on any platform.",
+  "type": "boolean"
+}
    • changedInput schema / properties / generate_image / description
      Previous value: -"Generate a matching image. Defaults to true. Adds 10 credits per post."New value: +"Generate a matching image for the post. Defaults to false over MCP; pass true to request one. Adds 10 credits per post."
    • addedInput schema / properties / preserve
      Added value: +{
+  "description": "Exact lines that must appear verbatim in the finished post — a closing question, a call to action, a tagline. The humanizer rewrites sentences, so anything whose exact wording matters belongs here rather than only in the topic.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already mark this as a non-read-only, open-world operation, and the description adds substantial context beyond them: it returns a job id immediately and must be polled via check_social_job with a 20-90 second expected duration, and it consumes credits. It stops short of covering edge cases such as credit behavior on failure, but the async contract and cost model are clearly disclosed.

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?

Four sentences, each earning its place: purpose, routing condition, sibling alternative, and the async/cost contract. The most decision-relevant content (what it does plus when not to use it) is front-loaded before the operational details.

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?

Although there is no output schema, the description covers what an agent needs to call and follow up on this tool: it returns a job id and must be polled with check_social_job. Combined with cost signals and full schema-level parameter documentation, there is no material gap for a 16-parameter generator.

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 16 parameters — including defaults, enums, ranges, and the 'omit for automatic' behavior — are already fully documented in the schema. The description adds no per-parameter meaning beyond what is structured, which is the expected baseline when the schema does the heavy lifting.

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?

States a specific verb and resource ('Write a social media post from scratch') and immediately scopes it by platform and topic. It explicitly names what it is not — a checker/fixer — and routes to the sibling tools score_text and humanize_text, so an agent can distinguish it from all four siblings without opening any schema.

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

Usage Guidelines5/5

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

Gives an explicit when-to-use condition ('when you do not already have a draft'), concrete examples (multiple variations, unfamiliar platform conventions), and an explicit when-not with the preferred alternatives ('If you have already written the post yourself, prefer score_text and humanize_text'). Nothing is left to inference.

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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