gwanjong-mcp 0.2.0

Stateful Pipeline MCP 서버 - 최소 토큰으로 개발자 커뮤니티 활동을 수행하는 AI 소셜 에이전트

gwanjong-mcp

Stateful Pipeline MCP server for AI social agents.

Engage developer communities authentically. Comment to connect, post to promote.

---

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

1. Minimal tools — 5 total. Tool descriptions are included in every system prompt, so fewer = cheaper + more accurate.
2. Server-side state — Scout results are cached on the server. The LLM doesn't relay data between tools.
3. Server as cerebellum — Fetching, filtering, scoring, and analysis happen inside the server. The LLM only handles judgment and content generation.
4. 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"

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

pytest                    # Tests
mypy gwanjong_mcp/        # Type check
ruff check gwanjong_mcp/  # Lint
python run.py             # Local server

License

MIT