Reddit Growth MCP

<p align="center"> <img src="assets/banner.png" alt="Reddit Growth MCP" width="480" /> </p>

<h1 align="center">Reddit Growth MCP</h1>

A toolkit for analyzing subreddits and the patterns behind posts that get accepted and perform well. It helps you choose the right communities and shape posts that fit each one, using real data rather than guesswork.

It runs as an MCP server (for use inside Claude, Cursor, and other MCP clients) and as a standalone command-line tool. Most analysis works without any Reddit API credentials, because it reads from a public historical archive.

<p align="center"> <img src="assets/demo-plan.png" alt="reddit-growth in action" width="760" /> </p>

One command turns a list of subreddits into a plan — where to post, what to post, and when:

$ reddit-growth plan singularity LocalLLaMA mcp --tz 7

GROWTH PLAN
Target: r/singularity
  growth 222.9 · viral 629.1 · 48 posts/day · 23 comments · 12% removed (safe)
  discussion depth (insight): medium (30% substantive)

Also worth posting to (safe, tailor per sub)
  r/LocalLLaMA   growth 64.3 · 12% removed (safe)

What to post (viral recipe)
  Format : image      Flair : AI      Length : ~97 chars, no clickbait
  Words  : open, kimi, deepseek

When to post
  08:00 UTC = 15:00 local / 15:00 UTC = 22:00 local   ·   Days: Friday, Saturday

Quick start

Requirements: Python 3.11+ and uv. No Reddit account or API keys are needed to get started.

Install from PyPI:

uvx reddit-growth-mcp                            # run the MCP server instantly
pipx install reddit-growth-mcp                   # or install the CLI + server
reddit-growth plan singularity LocalLLaMA mcp    # your first growth plan

Or from a clone (for development):

uv sync                                                    # install dependencies
uv run python -m src.cli plan singularity LocalLLaMA mcp   # your first growth plan

That prints where to post, what to post, and when — using only the public archive. Every command has the form uv run python -m src.cli <command> [options] (shown throughout as reddit-growth <command>, which is the installed alias). Add -h to any command for help, e.g. uv run python -m src.cli plan -h.

Interactive launcher

Prefer a menu to flags? Run:

bash scripts/menu.sh

Pick a mode (plan, compare, patterns, draft, …), choose subreddits from a searchable preset list, and it runs the command for you — all in one window. With gum installed you get an arrow-key TUI with fuzzy search; otherwise it falls back to a numbered menu. Type h for a built-in guide.

What it answers

• Which subreddits fit my topic, and how much reach do they have?
• Will my post survive here, or does this community remove a lot of posts?
• What actually performs here: which format, title style, timing, and flair?
• Given a specific draft, how is it likely to do, and how do I improve it?

Demo

compare ranks candidate subreddits by growth, viral ceiling, and removal risk:

Tools

Tool	Purpose	Needs credentials
analyze_post_patterns	What performs in a sub: timing, media, title style, flair, keywords, by a configurable metric	No
analyze_acceptance	Removal rate and what tends to get removed; official rules when credentials are present	No
compare_subreddits	Rank subreddits by growth (typical reach + viral upside), with traffic (posts/day), discussion, removal risk, and a safety label; rank_by switches to viral/opportunity/insight	No
analyze_insight	Discussion depth (comment substance) plus a heuristic sentiment read (supportive/mixed/critical) — not just comment count	No
growth_plan	One call: safest strong target, cross-post options, viral recipe, and best posting times	No
evaluate_draft	Predict a draft's performance (0-100) and acceptance risk, with drivers and fixes	No
evaluate_draft_across	Score one draft across subs, ranked by size-fair fit (percentile) vs raw reach	No
analyze_subreddit	Estimate a subreddit's activity (posts/day); uses the archive without credentials	No
find_target_subreddits_tool	Discover and rank subreddits for topics by estimated traffic	Yes
fetch_posts, fetch_multiple, search_subreddit, fetch_comments	Raw data access	Yes

The analysis tools are subreddit-agnostic — they work on any archived sub. The launcher ships 40+ presets across AI, dev, and startup communities (ChatGPT, DeepSeek, LocalLLaMA, StableDiffusion, SaaS, indiehackers, …).

Data sources

• PRAW for live Reddit access (read-only). Requires API credentials.
• Arctic Shift (https://github.com/ArthurHeitmann/arctic_shift), a public historical archive and the successor to Pushshift. Requires no credentials.

Removal detection follows the reveddit approach: the archive records what was posted, and moderator removals are read from that record. When Reddit credentials are available, analyze_acceptance performs an accurate live diff (archive vs. current Reddit) to resolve ambiguous cases; without credentials it runs archive-only and flags its confidence.

Reddit credentials (optional)

Everything above works with no account. Credentials only unlock the credential-only tools (raw data access, find_target_subreddits) and the accurate live-removal check. Create a "script" app at https://www.reddit.com/prefs/apps, then:

cp .env.sample .env
# REDDIT_CLIENT_ID=...
# REDDIT_CLIENT_SECRET=...
# REDDIT_USER_AGENT=reddit-growth-mcp/0.2 by u/your_username

Command-line usage

Every command runs credential-free from the archive. The commands are: traffic, insight, patterns, acceptance, compare, plan, report, draft, and fit (run -h on any of them for options).

uv run python -m src.cli traffic LocalLLaMA
uv run python -m src.cli insight mcp
uv run python -m src.cli patterns Fedora --time month
uv run python -m src.cli patterns commandline --metric discussion
uv run python -m src.cli acceptance technology
uv run python -m src.cli compare Fedora gnome linux
uv run python -m src.cli plan singularity LocalLLaMA mcp --tz 7
uv run python -m src.cli draft ClaudeAI --title "I built an ASCII art tool" --type image
uv run python -m src.cli fit singularity LocalLLaMA mcp --title "..." --type video

fit scores one draft across several subreddits and ranks by a size-fair fit (the draft's percentile within each sub's own score distribution) alongside raw expected reach, so a small sub where the post lands in the top decile isn't buried by a big sub's larger absolute numbers.

Add --json to any command for raw output.

Time-based commands (patterns, plan, draft, fit, report) accept --time day|week|month|year|all — shorter is fresher but a smaller sample.

patterns accepts --metric:

• score: upvotes (reach).
• comments: comment volume.
• discussion: comments per upvote, a proxy for genuine engagement rather than drive-by upvotes.
• quality: upvotes damped by a clickbait penalty.

Example analysis

Ranking a set of AI/dev communities for account growth (credential-free):

$ reddit-growth compare singularity LocalLLaMA unixporn linux mcp --window 30d

SUBREDDIT COMPARISON  (ranked by growth)
subreddit             growth   viral  posts/day  comments  removal    safety  conf
  r/singularity         222.9   629.1       48.7        22     12%      safe  ok
  r/LocalLLaMA           64.3   199.8       80.2        19     12%      safe  ok
  r/unixporn             53.4   120.0       38.5         2     28%  moderate  ok
  r/linux                12.8    42.6       60.9        10     55%    strict  ok
  r/mcp                   1.5     2.8       45.6         1     30%  moderate  ok

Reading it: r/singularity and r/LocalLLaMA are safe (low removal) and active, with high viral ceilings and real discussion; r/linux is active but strict (55% of posts removed). Turning that into a plan:

$ reddit-growth plan singularity LocalLLaMA unixporn mcp --tz 7

GROWTH PLAN
Target: r/singularity
  growth 222.9 · viral 629.1 · 48.7 posts/day · 22 comments · 12% removed (safe)

Also worth posting to (safe, tailor per sub)
  r/LocalLLaMA   growth 64.3 · 12% removed (safe)
  r/unixporn     growth 53.4 · 28% removed (moderate)

What to post (viral recipe)
  Format : link
  Flair  : AI
  Length : ~97.5 chars, no clickbait

When to post
  08:00 UTC = 15:00 local / 15:00 UTC = 22:00 local
  Days: Saturday, Friday

Figures are estimates from a sample and will shift over time; run it live for current numbers.

Use as an MCP server

Register the server once (Claude Code shown):

claude mcp add reddit-growth-mcp -- uvx reddit-growth-mcp

For Cline, Cursor, Claude Desktop, and other clients, add it to the MCP settings JSON (no separate install — uvx fetches it from PyPI):

{
  "mcpServers": {
    "reddit-growth": {
      "command": "uvx",
      "args": ["reddit-growth-mcp"]
    }
  }
}

Then ask in natural language, for example "analyze what performs in r/Fedora" or "will this title get accepted in r/linux?" The client calls the tools.

For the full flow in one step, ask for a growth plan ("build a growth plan for r/singularity, r/LocalLLaMA, r/mcp") to invoke growth_plan, or select the reddit_growth prompt, which guides the assistant through finding a safe high-traffic subreddit and crafting a post that fits its viral recipe.

To run the server directly over stdio:

uv run python -m src.server

Docker

The server also ships as a container (stdio transport):

docker build -t reddit-growth-mcp .
docker run -i --rm reddit-growth-mcp
# with credentials:
docker run -i --rm -e REDDIT_CLIENT_ID=... -e REDDIT_CLIENT_SECRET=... reddit-growth-mcp

Targeting workflow

To find where to post for growth, compare_subreddits reports, per subreddit:

• viral potential (90th-percentile reach adjusted for removal risk) and ceiling,
• posts per day (a credential-free traffic proxy),
• typical discussion (median comments),
• removal rate and a safety label (safe / moderate / strict), so you can avoid communities that remove most posts.

A typical flow: compare to shortlist safe, high-traffic, high-ceiling subs, then patterns to read the viral recipe, then evaluate_draft to score a draft against it before posting.

Reach and insight are different goals. compare counts comments (volume); analyze_insight measures their depth — median comment length and the share of substantive comments. A sub can have many short one-line replies (high volume, low insight) or fewer long technical comments (low volume, high insight). Use reach-oriented ranking for visibility, and analyze_insight to find where thoughtful discussion happens and reputation is built.

Accuracy and methodology

The tool is built to avoid the common failure modes of naive Reddit analytics.

• Robust central tendency. Categories (media, flair, time) are ranked by the median, with the mean shown for reference, so a single viral post cannot crown a category.
• Minimum-sample gating. A category or time bucket must contain enough posts to be reported as "best". Small buckets are not treated as reliable signals, and draft scoring ignores title signals whose with/without groups are too small, so a single lucky post cannot swing a projection or seed a bogus suggestion.
• Confidence labelling. Each pattern report states a confidence level based on its sample size, and each acceptance report states a reliability level.
• Settled scores. Archived scores stabilise after roughly 36 hours, so analysis excludes the most recent two days.
• AutoMod awareness. Posts that were only AutoMod-filtered at capture time are treated as uncertain, not confirmed removals, because they are frequently approved later. On AutoMod-heavy subreddits this is flagged, and an accurate live check requires credentials.
• Removal-aware verdicts. A draft's acceptance verdict folds in the sub's base removal rate, so a compliant post in a subreddit that removes most of what it gets is flagged risky rather than "likely accepted".
• Anti-clickbait. Clickbait titles are detected (hype phrases, shouted words, emoji and punctuation spam), and each report states whether the community actually rewards or penalises clickbait, so guidance never pushes you toward it. evaluate_draft penalises a clickbaity draft only where the sub dislikes it.

Limitations

• Traffic figures are estimates. Reddit does not expose true daily visitor counts through its public API.
• Findings are correlations from a sample, not Reddit's ranking algorithm and not a guarantee of performance.
• AutoModerator configuration is private. Karma and account-age gates are inferred from rule text and require credentials to read.

Development

uv sync --extra dev
uv run pytest -q                       # tests
uv run ruff check src tests            # lint
uv run ruff format src tests           # format

CI runs the lint, format check, and tests on every push and pull request.

The analysis logic lives in src/analysis/ (traffic, acceptance, patterns, draft, compare, arctic, helpers). The MCP surface is src/server.py and the CLI is src/cli.py.

License

MIT

_{mcp-name: io.github.latent-9/reddit-growth-mcp}