mcpField
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SpeedContent Social

SpeedContent Social

Remote
github.com/seotraderSocial Media0Updated 4w ago
Repository

About SpeedContent Social

SpeedContent Social is a remote MCP server for drafting and refining social posts for seven platforms. It adds platform-specific styles, AI-detection scoring, humanization and optional matching images.

Key features

  1. 01Draft posts for seven platforms
  2. 02Rewrite text to sound human-authored
  3. 03Score text with an AI detector
  4. 04Preserve specified brand terms and lines
  5. 05Generate optional post illustrations

Use cases

  1. 01Drafting platform-specific social posts
  2. 02Checking existing copy for AI-detection scores
  3. 03Humanizing text while retaining brand wording

MCP support

Exposes tools to MCP clients for generating, scoring and humanizing social posts, checking generation jobs and listing platform rules.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://speedcontent.online/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://speedcontent.online/mcpAuth required

Tools

5
ParameterTypeRequiredDescription
toneenumNoDefaults to friendly.casual | educational | friendly | humorous | inspirational | professional
emojibooleanNoAdd emoji to the post. Omit for automatic: off on LinkedIn and YouTube, on elsewhere. Set false for a clean post on any platform.
topicstringYesWhat the post should be about.
humanizebooleanNoRewrite 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.
languagestringNoOutput language by name. Defaults to English.
platformenumYesTarget platform. Each has its own style guide and default length.facebook | instagram | linkedin | pinterest | tiktok | twitter | youtube
preservearrayNoExact 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.
quantityintegerNoHow many distinct variations to generate. Defaults to 1.
detect_aibooleanNoScore 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_namestringNoBrand to mention where it reads naturally.
brand_tonestringNoVoice guidance, e.g. 'Direct, no jargon'.
word_countintegerNoTarget length per post. Omit to use the platform default (LinkedIn 100, YouTube 150, Facebook/Pinterest 75, Instagram/TikTok 50, X 40).
brand_audiencestringNoWho the post should speak to.
brand_keywordsstringNoComma-separated terms to work in.
generate_imagebooleanNoGenerate a matching image for the post. Defaults to false over MCP; pass true to request one. Adds 10 credits per post.
brand_descriptionstringNoWhat the business does.

Details

From the project README

SpeedContent Social MCP

Give an AI agent the ability to write social media posts that don't read like AI wrote them.

An MCP server for the SpeedContent Social Post API. Agents can draft posts for seven platforms; each one is written to that platform's conventions, rewritten to read as human-authored, scored against AI detection, and optionally illustrated.

Listed in the official MCP Registry as io.github.seotrader/speedcontent-social.

Why this and not a plain model call

Any model can write a LinkedIn post. The difference is what happens after:

StepWhat it does
GenerateWritten against the platform's own style guide, not one prompt reskinned
HumanizeRewritten so it doesn't read as machine-written
DetectScored 0–65 by an AI detector; 0 reads as human
EmojiPlatform-aware; suppressed on LinkedIn and YouTube
Re-humanizeAutomatic retry if the score comes back too high
ImageOptional, generated to match the post

The scoring pass is the point. The agent is told how human the copy reads before anything is published.

Setup

This is a remote server — there is nothing to install. Get an API key at app.speedcontent.online/APIKeys (free credits on signup, no card), then point your client at the URL.

Claude Code
claude mcp add --transport http speedcontent-social \
  https://speedcontent.online/mcp \
  --header "X-API-Key: sc_your_key_here"
Claude Desktop, Cursor, and other clients
{
  "mcpServers": {
    "speedcontent-social": {
      "url": "https://speedcontent.online/mcp",
      "headers": {
        "X-API-Key": "sc_your_key_here"
      }
    }
  }
}

Authorization: Bearer sc_... works too, for clients that prefer it.

Tools

If you have already written the post, use score_text and humanize_text — they check and fix your own wording. Use generate_social_post when you need a draft written from scratch.

score_text

Score any text against an AI detector. Returns 0–65, where 0 reads as human and above 30 is likely to be flagged. This is the measurement an agent cannot make for itself. 8 credits.

humanize_text

Rewrite text so it no longer reads as machine-written. Brand names, domains and anything listed in preserve are restored verbatim afterwards, because the humanizer rewrites whole sentences and splits names. 1 credit per 20 words, plus 8 if you pass score_after.

ParameterNotes
textRequired. At least 10 characters.
preserveExact lines that must survive — a tagline, a call to action, a closing question.
brand_termsNames the humanizer must not alter or split.
score_afterScore the result and return it. Adds 8 credits.
generate_social_post

Writes a post from scratch. Starts a job and returns its id; collect the result with check_social_job. Costs credits.

ParameterDefaultNotes
topic—Required. What the post is about.
platform—Required. facebook, instagram, twitter, linkedin, pinterest, youtube, tiktok.
tonefriendlyprofessional, casual, friendly, humorous, inspirational, educational.
languageEnglishAny language name.
quantity1Up to 10 variations.
word_countper platform10–500. LinkedIn 100, YouTube 150, Facebook/Pinterest 75, Instagram/TikTok 50, X 40.
generate_imagefalse over MCPAdds 10 credits per post.
detect_aiautomaticAdds 8 credits per post. Automatic means 150+ words only — short posts score unreliably.
humanizeautomaticAutomatic scores the draft first and humanizes only if it would be flagged, which keeps your wording and saves a credit.
emojiautomaticOff on LinkedIn and YouTube, on elsewhere.
preserve—Exact lines that must appear verbatim.
brand_name, brand_description, brand_tone, brand_audience, brand_keywords—Optional brand voice.
check_social_job

Returns the finished posts, or current progress while the job runs. Free.

list_social_platforms

Platforms with default lengths and emoji policy. Free, no network call.

Credits

Per post: 20 base (up to 200 words), +1 per 10 words beyond 200, +10 for an image, +8 for AI detection. Multiplied by quantity. Refunded in full if generation fails.

A 100-word LinkedIn post with an image costs 30 credits.

What's in this repo

The live server runs inside the SpeedContent API service. This repo holds the registry manifest and a standalone stdio implementation.

PathWhat it is
server.jsonThe MCP Registry manifest, pointing at the hosted endpoint
src/index.tsA standalone stdio server, for clients that can't do remote HTTP
PUBLISHING.mdHow this gets published and listed

The stdio build is not on npm and isn't needed for normal use. It exists as a fallback for older MCP clients that only support subprocess transport, and carries the same five tools:

npm install && npm run build
SPEEDCONTENT_API_KEY=sc_... node dist/index.js

Reselling

Volume and white-label pricing is per post rather than per seat, and your users don't need SpeedContent accounts of their own. Get in touch.

Licence

MIT

Comments

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