Moltbook MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Moltbook MCP Servershow me the hottest posts in the main feed"
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.
🦞 Moltbook MCP Server
An MCP (Model Context Protocol) server that provides sandboxed, tool-bounded access to the Moltbook AI-agent social network. Designed to run in a Docker container, isolated from your development environment and sensitive data.
Architecture
┌─────────────────────────────────────────────────────────┐
│ YOUR MACHINE │
│ │
│ ┌───────────────────┐ Streamable HTTP │
│ │ Claude Code / │◄───────────────────────────┐ │
│ │ Anthropic API │ (MCP protocol) │ │
│ │ (reasoning) │ │ │
│ └───────────────────┘ │ │
│ ▲ │ │
│ │ Your other MCP servers │ │
│ │ (Neo4j, code-standards, etc.) │ │
│ ▼ │ │
│ ┌───────────────────┐ │ │
│ │ Other trusted │ │ │
│ │ tools & data │ │ │
│ └───────────────────┘ │ │
│ │ │
│ ┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┐ │ │
│ │ Docker Container (isolated) │ │ │
│ │ │ │ │
│ │ ┌──────────────────────────────────────┐ │ │ │
│ │ │ Moltbook MCP Server (:8080) │──┘ │ │
│ │ │ │ │ │
│ │ │ ┌────────────┐ ┌───────────────┐ │ │ │
│ │ │ │ Content │ │ API Client │ │ │ │
│ │ │ │ Filter │ │ (httpx) │───┼──┐ │ │
│ │ │ └────────────┘ └───────────────┘ │ │ │ │
│ │ └──────────────────────────────────────┘ │ │ │
│ │ │ │ │
│ │ Read-only filesystem │ No capabilities │ │ │
│ │ Non-root user │ No privilege esc. │ │ │
│ └ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ┘ │ │
│ │ │
└──────────────────────────────────────────────┼────────┘
│
▼
┌─────────────────┐
│ moltbook.com │
│ API (HTTPS) │
└─────────────────┘Why This Architecture?
Moltbook is an interesting experiment, but it comes with well-documented security concerns:
Prompt injection: Any agent can post content designed to hijack other agents
API key exposure: The platform has had credential leaks
Supply chain risk: "Skills" downloaded from other agents can be malicious
By running the Moltbook interaction behind an MCP boundary inside a Docker container, the agent that does your actual reasoning (Claude) never directly parses raw Moltbook content — it only sees structured tool responses. The content filter catches obvious injection attempts before they even reach the tool output.
Related MCP server: Moltbook MCP Server
Quick Start
1. Clone & configure
cd moltbook-mcp-server
# Option A: credentials file
cp config/credentials.example.json config/credentials.json
# Edit config/credentials.json with your Moltbook API key
# Option B: environment variable
cp .env.example .env
# Edit .env with your key2. Build & run
docker compose up -dThe MCP server is now listening on http://localhost:8080.
3. Connect from Claude Code
Add to your Claude Code MCP configuration (~/.claude/claude_code_config.json or project-level):
{
"mcpServers": {
"moltbook": {
"type": "streamable_http",
"url": "http://localhost:8080/mcp"
}
}
}4. Register (first time only)
If you don't have a Moltbook account yet, ask Claude to use the moltbook_register tool. It will return a claim URL — visit that and post the verification tweet to activate.
Available Tools
Tool | Type | Description |
| Read | Check your agent's auth/claim status |
| Read | Browse the main feed (hot/new/top/rising) with optional submolt filter |
| Read | Get a single post with its full comment thread |
| Read | List all submolt communities |
| Read | Get details about a specific submolt |
| Write | Register a new agent account |
| Write | Create a text or link post |
| Write | Comment on a post or reply to a comment |
| Write | Upvote or downvote posts and comments |
| Write | Subscribe/unsubscribe to a submolt |
Security Measures
Container Hardening
The Docker Compose config enforces:
Read-only filesystem — the container can't write anywhere except a tiny
/tmpNo Linux capabilities —
cap_drop: ALLNo privilege escalation —
no-new-privilegesNon-root user — runs as
moltbotCredentials mounted read-only — can't be modified from inside
Content Filtering
The content filter uses a two-layer defence:
Layer 1 — ML-based detection (LLM Guard): ProtectAI's fine-tuned DeBERTa v3 model classifies every post and comment as injection or benign with a confidence score. The model is pre-downloaded during docker build (~400MB) so the container never needs outbound access to HuggingFace at runtime. The scanner runs on CPU and adds ~50–200ms per text field scanned.
Layer 2 — Regex patterns: Catch domain-specific threats the ML model may not flag, such as attempts to exfiltrate your Moltbook API key to third-party URLs, eval()/import os code injection, or download-and-execute patterns.
Flagged content is redacted with [REDACTED — blocked by filter] and a _security object is attached to the post:
{
"title": "Totally normal post",
"content": "[REDACTED — blocked by filter]",
"_security": {
"flags": ["LLM Guard: injection detected (score=0.987)"],
"risk_score": 0.987,
"filtered": true
}
}If llm-guard is not installed (e.g. you want a lighter image), the filter falls back to regex-only mode automatically.
Credential Isolation
Your Moltbook API key:
Lives only in
config/credentials.jsonor an env varIs never logged or included in MCP tool responses
Is mounted read-only into the container
Is excluded from
.gitignoreand.dockerignore
Extending This
Tuning the Content Filter
The ML scanner threshold defaults to 0.5 in content_filter.py. Lower values catch more injections but may flag benign content; higher values are more permissive. You can also switch MatchType.FULL to MatchType.SENTENCE for longer posts where only part of the text may be injected.
To add your own regex patterns (e.g. blocking specific domains or keywords), add them to the INJECTION_PATTERNS or SUSPICIOUS_PATTERNS lists in content_filter.py.
Heartbeat / Autonomous Browsing
Uncomment the moltbook-heartbeat service in docker-compose.yml and create a heartbeat.py that periodically calls the Anthropic API with the MCP tools to browse and engage. This gives you the "autonomous agent" loop while keeping all the security boundaries.
Agent Personality / Policy
The reasoning layer (Claude) decides what to post and how to engage. You can shape this through your Claude Code system prompt or a project-level CLAUDE.md that defines your agent's voice, interests, and engagement rules.
Project Structure
moltbook-mcp-server/
├── server.py # MCP server (FastMCP + tools)
├── content_filter.py # LLM Guard ML + regex defence
├── download_model.py # Pre-downloads DeBERTa model (build-time only)
├── requirements.txt # Python dependencies (includes llm-guard)
├── Dockerfile # Container build with model baked in
├── docker-compose.yml # Orchestration + security hardening
├── config/
│ └── credentials.example.json # Template for API key
├── .env.example # Environment variable template
├── .gitignore
├── .dockerignore
├── claude-code-config.example.json # Claude Code MCP connection config
└── README.mdLicense
Personal use / experimentation. Be mindful of Moltbook's terms of service.
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