Rug Munch

Provides rug-munch token risk intelligence tools via MCP with local or client-based integration.

python

0

GitHub Stars

python

Language

4 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "cryptorugmunch-rug-munch-mcp": {
      "command": "rug-munch-mcp",
      "args": [],
      "env": {
        "RUG_MUNCH_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

Rug Munch MCP Server provides a programmable interface to run rug-munch-mcp analysis tools within your MCP client workflow. You can install, run, and integrate token risk intelligence, AI forensics, social signals, and market context into your automated checks before transacting with crypto projects.

How to use

You use the Rug Munch MCP Server by running the MCP backend locally and connecting your MCP client (Claude Desktop, Cursor, Windsurf, or any MCP-compatible client) to it. Start the server, then invoke the available tools through your MCP client as you would with any other MCP server. The server handles paid tools via x402 micropayments when enabled, or can operate with a provided API key to bypass payments. The tools return a risk assessment, context, and actionable recommendations you can act on within your workflow.

How to install

Prerequisites: Python and a functioning Python package environment. You may also use a prebuilt CLI in your MCP client workflow.

# Install the Rug Munch MCP package
pip install rug-munch-mcp

# Start the MCP server from the package (CLI mode)
rug-munch-mcp
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Additional notes

Configuration is shown in the usage examples for different MCP clients. If you prefer to bypass micropayments, provide an API key in your MCP client configuration as shown in the payment setup. The API key enables direct access to tools without performing x402 payments.

Supported environments include running as a standard input/output server or integrating through Python module execution. When using a client that launches the server via Python, you typically start the server with an invocation like python3 -m rug_munch_mcp. When using a CLI-based client, you can start the server directly with rug-munch-mcp and connect through MCP.

Security and usage tips

Do not expose keys in public code repositories.
Rotate keys if you suspect compromise.

Available tools

check_token_risk

Quick risk score (0-100) and a recommendation for action.

check_token_risk_premium

Full breakdown with deployer history and social OSINT.

check_batch_risk

Risk analysis for up to 20 tokens in a single run.

check_deployer_history

Analysis of the deployer wallet to detect serial ruggers.

get_token_intelligence

Complete token profile including price, holders, and liquidity pools.

get_holder_deepdive

Top holders, concentration, and sniper detection.

get_social_osint

Social media presence and red flags for the token.

get_kol_shills

Detection of influencer shills and promotion activity.

get_coordinated_buys

Detection of coordinated buying patterns.

check_blacklist

Community blacklist checks.

check_scammer_wallet

Known scammer wallet checks.

get_market_risk_index

Global market risk overview.

get_serial_ruggers

Known serial rugger wallets.

marcus_quick

AI quick verdict using Claude Sonnet 4 for rapid insights.

marcus_forensics

AI forensics — full Claude Sonnet 4 investigation.

marcus_ultra

AI deep analysis using Claude Opus 4 for in-depth review.

marcus_thread

AI analysis thread for X/Twitter posts.

watch_token

7-day webhook monitoring for risk changes.

get_api_status

Service health and accuracy metrics (free).