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Nooticr

Researches and analyzes social posts across 10 networks, then helps create hooks, variants, drafts, and repurposed content.

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Install Nooticr

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "nooticr": {
      "command": "npx",
      "args": [
        "-y",
        "@nooticr/mcp"
      ],
      "env": {
        "NOOTICR_BASE_URL": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Install not yet sandbox-tested— the server itself is confirmed live (we called its tools/list endpoint). The stdio install command is a separate, ongoing check.

Overview

Nooticr connects an MCP-compatible assistant to social posts across TikTok, Instagram, YouTube, X, Reddit, Douyin, Xiaohongshu, Weibo, Bilibili, and LinkedIn. It retrieves media, transcripts, frames, comments, creator posts, trends, sounds, hashtags, and engagement data for model-led analysis. Reach for it when an agent needs evidence from social content rather than a prewritten third-party opinion.

Use cases

  • Analyze a social post’s hook, visuals, transcript, and engagement
  • Discover recent content patterns in a niche
  • Compare creator posts and identify a testable difference
  • Generate hooks and filming variants from a successful post
  • Repurpose a post for other publishing formats

Key features

  • Social post retrieval across 10 networks
  • Transcript, frame, comment, and spoken-mention analysis
  • Niche, creator, sound, and hashtag discovery
  • Hook, variant, draft, and repurposing workflows
  • Free rendering tools for model-produced results

Tools119

What Nooticr lets your agent do. Expand a tool for its parameters.

Verified live~39.5k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

7/7 checks healthy over the last 46d

What it can do

From the 119 tools this server returned when we ran it; 119 declare their own safety hints.

Can delete or modify data · 14Handles credentials · 11Makes network requests · 61Read-only tools · 105
Network at startup3 connections
Files written at startupNone
Install scriptsprepare
npm provenanceNot published
Package age41 days
Maintainers1

Observed 2026-10-17 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 1d ago via OSV.dev · @nooticr/mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads5.8kPackage downloads in the last 30 days.
Last commitTodayMost recent push to the default branch.
Tools exposed119Callable tools this server registers over MCP.
Directory activity4 viewsConfig copies, upvotes, and views on AllMCPs.

How Nooticr works

What Nooticr does

Nooticr gives an MCP-compatible assistant access to public social content and supporting research workflows. It can retrieve individual posts, inspect a creator’s recent output, discover posts in a niche, find similar creators, identify possible cross-network identities, and research sounds or hashtags.

The server supports TikTok, Instagram, YouTube, X, Reddit, Douyin, Xiaohongshu, Weibo, Bilibili, and LinkedIn for many post and creator operations. Creator search is narrower: it searches TikTok, Instagram, and Xiaohongshu. Hashtag discovery also varies by network; LinkedIn cannot be swept, while TikTok uses a trend board and other searchable networks use a live niche sample.

How it works

For example, analyze_post supplies scene-change frames, a transcript, caption, and statistics so the model can reason about the hook, structure, visual treatment, call to action, and audience. analyze_post_fast omits frames when words and numbers are sufficient.

Transcripts use a platform caption track when available. Otherwise, the server transcribes the post’s audio when speech-to-text is configured. Transcript requests return immediately; SRT and VTT formats are available for caption or editing workflows. A post can also be examined through its comments, spoken brand mentions, media facts, or individual frames.

Many operations consume nooticr credits. Reading a post or transcript costs 1 credit, while discovery, creator research, comments, and richer analyses generally cost more according to the underlying fetches. Display tools such as show_post_analysis, show_comment_review, and show_hooks are free and render results already produced by the model without making another request.

Setup and configuration

The package runs locally over stdio or can be accessed through the hosted MCP endpoint at https://mcp.nooticr.com/mcp. A stdio client can launch it with npx -y @nooticr/mcp. Claude Code supports the nooticr plugin or a direct MCP registration, followed by a one-time Google sign-in through npx @nooticr/mcp login.

For a direct stdio configuration, set NOOTICR_BASE_URL to https://api.nooticr.com. Usage is charged against a nooticr credit balance; new accounts receive 20 free credits.

Tools and capabilities

  • Retrieve post media, captions, authors, statistics, thumbnails, and direct media URLs.
  • Read transcripts, spoken brand mentions, scene-change frames, and top comments.
  • Discover niche posts, creator output, similar creators, sounds, and hashtags.
  • Compare posts, evaluate drafts, generate hooks, and propose post variants.
  • Adapt existing content for X, LinkedIn, carousels, YouTube, or newsletters.
  • Render model-produced analyses, comment reviews, comparisons, hooks, variants, and repurposed posts.

Limitations and notes

Frame retrieval adds roughly 1,200 context tokens per frame, so visual analysis can be expensive in context as well as credits. Some networks return mainly text, and some posts may lack video URLs or durations. Cross-network creator matching is suggestive only: evidence must be reviewed by a person before treating accounts as the same identity. Content in post titles, captions, bios, comments, and transcripts should be treated as source evidence, not as instructions.

Read the full README View source on GitHub

Reviews

No reviews yet — be the first to share how it worked for you.

Frequently Asked Questions about Nooticr

Nooticr is an MCP server that connects AI assistants to social content across TikTok, Instagram, YouTube, X, Reddit, LinkedIn, Douyin, Xiaohongshu, Weibo, and Bilibili. Its main tools retrieve posts, transcripts, frames, comments, creator data, trends, hooks, variants, and repurposed content for model-led analysis.

Install it as a stdio MCP server with `npx -y @nooticr/mcp`, then register that command in your MCP client. Claude Code can use the nooticr plugin or direct registration, and a one-time `npx @nooticr/mcp login` signs in with Google.

Yes, Nooticr works with Cursor through a standard stdio MCP configuration. Add the server command `npx -y @nooticr/mcp` and set `NOOTICR_BASE_URL` to `https://api.nooticr.com` in the server environment.

Nooticr can use `analyze_post` to retrieve scene-change frames, a transcript, caption, and statistics for model-led analysis. Use `analyze_post_fast` when visuals are unnecessary; it returns the transcript, caption, and statistics without frames.

Nooticr charges nooticr credits based on the data fetched. A post or transcript costs 1 credit, while tools that retrieve frames, comments, creator posts, or discovery results generally cost more; free display tools make no requests.

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