Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| RUG_MUNCH_API_KEY | No | API key to bypass x402 micropayments. |
Capabilities
Features and capabilities supported by this server
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| check_token_risk | CRITICAL: Check a token's rug pull risk score BEFORE any transaction. Returns 0-100 risk score, honeypot detection, deployer history, freeze authority check, holder concentration, and SAFE/CAUTION/AVOID recommendation. Cost: $0.04 per check. The cheapest insurance in crypto. |
| check_token_risk_premium | Premium deep risk analysis with full category breakdown, deployer cross-reference, social OSINT, holder intelligence, and historical pattern matching. Use for high-value positions. Cost: $0.10. |
| check_batch_risk | Batch risk check for up to 20 tokens at once. Returns risk scores and recommendations for all tokens. Ideal for portfolio screening. Cost: $0.30 (~$0.015 per token). |
| check_deployer_history | Check a token deployer's full history: tokens deployed, rug count, classification (legitimate_builder / suspicious / serial_rugger). Essential for evaluating new token trustworthiness. Cost: $0.06. |
| get_token_intelligence | Comprehensive token data: price, volume, market cap, holder stats, LP lock status, authority flags, buy/sell ratios, and top holders. Cost: $0.06. |
| get_holder_deepdive | Deep holder analysis: sniper detection, Jito bundle analysis, fresh wallet clustering, whale concentration, connected wallet patterns. Detects coordinated manipulation. Cost: $0.10. |
| get_social_osint | Social infrastructure analysis: Twitter account recycling, domain age, Telegram group legitimacy, cross-references with known scam infrastructure. Cost: $0.06. |
| get_kol_shills | KOL shill pattern detection: which influencers promote this token, their buy timing vs shill timing, coordinated pump patterns. Cost: $0.06. |
| get_coordinated_buys | Detect coordinated buying across tracked KOLs. Identifies tokens where multiple influencers bought within a short window. Cost: $0.04. |
| check_blacklist | Check if a token is community-flagged. Community-sourced intelligence with reputation-weighted flags. Cost: $0.02. |
| check_scammer_wallet | Check if a wallet belongs to a known scammer, serial rugger, or flagged entity. Cost: $0.02. |
| get_market_risk_index | Daily market-wide rug risk index (0-100). Components: high_risk_ratio, rug_velocity, liquidity_drains, deployer_activity. High = more rugs happening = exercise caution. Cost: $0.02. |
| get_serial_ruggers | Known serial rug deployer watchlist. Cross-reference before trusting new tokens. Cost: $0.02. |
| marcus_quick | AI forensic verdict by Marcus Aurelius (Claude Sonnet 4). One-paragraph analysis with risk score, key flags, and Stoic wisdom. ~5-30s latency. Cost: $0.15. |
| marcus_forensics | Full AI forensic investigation by Marcus Aurelius (Claude Sonnet 4). Covers deployer history, holder patterns, social OSINT, KOL cross-reference, contract security, liquidity, and trading patterns. ~15-60s. Cost: $0.50. |
| marcus_ultra | The deepest AI forensic analysis available. Powered by Claude Opus 4 with extended reasoning. Catches subtle patterns: connected wallet clusters, timing correlations, historical rug playbook matching. ~30-120s. Cost: $2.00. |
| marcus_thread | X/Twitter-thread-ready forensic analysis. 5-8 posts, each ≤280 chars. Perfect for research posting. Cost: $1.00. |
| watch_token | Set up real-time token monitoring with webhook alerts. When risk changes, rug detected, or price drops, we POST to your webhook. Covers 7 days. No other service offers proactive rug detection. Cost: $0.20. |
| get_api_status | Get service status, performance metrics, trust score, and pricing. FREE — no payment required. Use this to verify the service is up before making paid calls. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |