Which integrations are available for this server?

Enables posting and engaging with content on Bluesky via the AT Protocol, supporting social media interactions and community building. Allows posting articles and comments on Dev.to, enabling engagement with developer communities through helpful discussions and content sharing. Provides the ability to post comments and content on Reddit, allowing interaction with subreddits and participation in discussions.

How do I use gwanjong-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@gwanjong-mcp Find interesting MCP discussions and join in" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

gwanjong-mcp

by SonAIengine

Overview Schema Related Servers Score Discussions

Python

Remote

gwanjong-mcp

AI Growth OS — Campaign-driven social engagement MCP server.

Engage developer communities authentically. Track everything by campaign.

License: MIT Python 3.10+ Test Lint PyPI

English · 한국어

Quick Start

# 1. Install
pip install "gwanjong-mcp[all]"

# 2. Configure at least one platform
mkdir -p ~/.gwanjong
cat > ~/.gwanjong/.env << 'EOF'
DEVTO_API_KEY=your_key_here
EOF

# 3. Verify setup
gwanjong-mcp  # starts MCP server (use with Claude Code, Cursor, etc.)

With Claude Code:

claude mcp add gwanjong-mcp -- gwanjong-mcp
claude
> "Find interesting MCP discussions and leave a helpful comment"

Autonomous mode (no LLM client needed):

pip install "gwanjong-mcp[all,autonomous]"
gwanjong-daemon --topics "MCP,LLM" --dry-run --max-cycles 1

See .env.example for all configuration options.

Related MCP server: mcp-linkedin

Philosophy

Two modes, one goal: building genuine presence in developer communities.

Mode	Action	Goal
Comment	Reply to others' posts	Earn reputation through helpful, authentic engagement. No self-promotion.
Post	Publish original content	Share your projects, write-ups, and announcements. This is where promotion lives.

Comments are for giving value — answering questions, sharing insights, joining discussions. The community notices when someone is genuinely helpful. Posts are for showing your work — project launches, technical deep dives, lessons learned.

Why This Exists

Typical MCP servers expose CRUD tools and let the LLM orchestrate everything. Leaving a single comment requires 9+ tool calls, 9+ LLM round trips, and the full tool description list resent every time.

Traditional MCP (14 tools, 9+ round trips):
LLM → list → LLM → trending → LLM → search → LLM → analyze → LLM → get_post
→ LLM → get_comments → LLM → preview → LLM → write → LLM

gwanjong-mcp (5 tools, 3 round trips):
LLM → scout → LLM → draft → LLM (generates content) → strike → done

Design Principles

Minimal tools — 5 total. Tool descriptions are included in every system prompt, so fewer = cheaper + more accurate.
Server-side state — Scout results are cached on the server. The LLM doesn't relay data between tools.
Server as cerebellum — Fetching, filtering, scoring, and analysis happen inside the server. The LLM only handles judgment and content generation.
Compressed returns — Never dump 20 raw posts. The server scores and returns the top N as summaries.

MCP Tools

Tool	Role	What happens inside
`gwanjong_setup`	Onboarding	Check platform status → guide API key setup → save + test connection
`gwanjong_scout`	Reconnaissance	Trending + search + analyze + score → return top N opportunities
`gwanjong_draft`	Context gathering	Fetch target post + comment tree + tone analysis → return context summary
`gwanjong_strike`	Execution	Post comment/article/cross-post → return result URL
`_status`	Pipeline state	Show state fields + available/blocked tools (auto-generated by mcp-pipeline)

Pipeline Flow

scout(topic, platforms)
  │  stores → opportunities
  │  Server internally: fetch trending + search + score + filter
  │  Returns: top N scored opportunities (~200 tokens)
  ▼
draft(opportunity_id)
  │  requires → opportunities
  │  stores → contexts
  │  Server internally: fetch post + comments + analyze tone
  │  Returns: context summary + suggested approach (~300 tokens)
  ▼
  LLM generates content based on context
  ▼
strike(opportunity_id, action, content)
     requires → contexts
     Server internally: write via platform API + record history
     Returns: { url, status }

Example: Commenting (Engagement)

User: "Find interesting MCP discussions and join in"

[1] scout(topic="MCP server", platforms=["devto", "reddit"])
    → Server scans trending + search across platforms → scores → returns top 3
    {
      "opportunities": [
        {"id": "opp_0", "platform": "devto",
         "title": "Best MCP servers for productivity?",
         "relevance": 0.91, "comments": 42,
         "reason": "Active discussion, directly relevant"}
      ]
    }

[2] draft(opportunity_id="opp_0")
    → Server fetches full post + comment tree + tone
    {
      "title": "Best MCP servers for productivity?",
      "body_summary": "...",
      "top_comments": ["...", "..."],
      "tone": "technical, recommendation-seeking",
      "suggested_approach": "Share genuine experience, no self-promo"
    }
    → LLM crafts a helpful, authentic reply

[3] strike(opportunity_id="opp_0", action="comment", content="...")
    → {"url": "https://dev.to/.../comment/...", "status": "posted"}

Example: Posting (Promotion)

User: "Write a post about mcp-pipeline on Dev.to"

[1] scout(topic="MCP token optimization", platforms=["devto"])
    → Find what's trending to inform angle and timing

[2] draft(opportunity_id="opp_0")
    → Gather context on existing coverage

[3] LLM writes an original article about the project

[4] strike(opportunity_id="opp_0", action="post", content="...")
    → {"url": "https://dev.to/sonaiengine/...", "status": "posted"}

Supported Platforms

Platform	Protocol	Auth
Dev.to	REST API (httpx)	API Key
Bluesky	AT Protocol	App Password
Twitter/X	OAuth 1.0a (tweepy)	API Key + Token
Reddit	OAuth2 (asyncpraw)	Client ID + Secret

Only platforms with configured API keys are activated. Others are silently skipped.

Install

# All platforms
pip install "gwanjong-mcp[all]"

# Specific platforms
pip install "gwanjong-mcp[devto]"
pip install "gwanjong-mcp[bluesky]"
pip install "gwanjong-mcp[twitter]"
pip install "gwanjong-mcp[reddit]"

# Development
git clone https://github.com/SonAIengine/gwanjong-mcp.git
cd gwanjong-mcp
pip install -e ".[all,dev]"

Environment Variables

Copy .env.example to .env and fill in the platforms you use:

# Dev.to — https://dev.to/settings/extensions
DEVTO_API_KEY=

# Bluesky — https://bsky.app/settings → App Passwords
BLUESKY_HANDLE=your.handle.bsky.social
BLUESKY_APP_PASSWORD=

# Twitter/X — https://developer.x.com/en/portal/dashboard
TWITTER_API_KEY=
TWITTER_API_SECRET=
TWITTER_ACCESS_TOKEN=
TWITTER_ACCESS_SECRET=

# Reddit — https://www.reddit.com/prefs/apps
REDDIT_CLIENT_ID=
REDDIT_CLIENT_SECRET=
REDDIT_USERNAME=
REDDIT_PASSWORD=

Claude Code Integration

# Register MCP server
claude mcp add gwanjong-mcp -- gwanjong-mcp

# Use with the gwanjong agent (~/.claude/agents/gwanjong.md)
claude agent gwanjong
> "Find interesting AI agent discussions and leave helpful comments"
> "Write a Dev.to post about mcp-pipeline"

Approval Workflow

Autonomous mode can stop before posting and enqueue generated content for review.

# Queue content instead of posting immediately
gwanjong-daemon --require-approval --max-cycles 1

# Review pending items
gwanjong-approval list
gwanjong-approval show 1

# Approve and execute strike immediately
gwanjong-approval approve 1

# Reject without posting
gwanjong-approval reject 2

If you run the dashboard, pending approvals are also visible and actionable from the UI:

gwanjong-dashboard
# open http://localhost:8585

Architecture

gwanjong-mcp/
├── pyproject.toml
├── run.py                         # Direct execution entry point
└── gwanjong_mcp/
    ├── __init__.py
    ├── __main__.py                # python -m gwanjong_mcp
    ├── server.py                  # PipelineMCP + 5 tools + GwanjongState
    ├── setup.py                   # Platform onboarding (guide/save/test)
    └── pipeline.py                # scout/draft/strike pipeline logic

Dependency Structure

┌─────────────────────────────────────────────────┐
│  Claude Agent (gwanjong.md)                     │
│  Persona · Content generation · Final judgment  │
└──────────────┬──────────────────────────────────┘
               │ 5 tools
┌──────────────▼──────────────────────────────────┐
│  gwanjong-mcp (this project)                    │
│  scout/draft/strike pipeline logic              │
│                                                 │
│  Dependencies:                                  │
│  ├── mcp-pipeline  — Stateful MCP framework     │
│  ├── devhub        — Multi-platform social API  │
│  └── graph-tool-call — Content search engine    │
└─────────────────────────────────────────────────┘

Package	Role	Install
devhub-social	Unified async client for Dev.to, Bluesky, Twitter, Reddit	`pip install devhub-social[all]`
mcp-pipeline	Type-safe state + declarative `stores`/`requires` tool chaining	`pip install mcp-pipeline`
graph-tool-call	BM25 + graph expansion + wRRF content scoring	`pip install graph-tool-call`

Development

./.venv/bin/python -m pytest -q                    # Default test suite (integration 제외)
./.venv/bin/python -m pytest -m integration -q    # Playwright/network integration tests
./.venv/bin/python -m mypy gwanjong_mcp/          # Type check
./.venv/bin/python -m ruff check gwanjong_mcp/    # Lint
./.venv/bin/python run.py                         # Local server

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

Need Help?

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