Rug Munch Intelligence
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. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| check_token_riskA | 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_premiumB | 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_riskA | 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_historyA | 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_intelligenceB | 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_deepdiveA | Deep holder analysis: sniper detection, Jito bundle analysis, fresh wallet clustering, whale concentration, connected wallet patterns. Detects coordinated manipulation. Cost: $0.10. |
| get_social_osintC | Social infrastructure analysis: Twitter account recycling, domain age, Telegram group legitimacy, cross-references with known scam infrastructure. Cost: $0.06. |
| get_kol_shillsB | KOL shill pattern detection: which influencers promote this token, their buy timing vs shill timing, coordinated pump patterns. Cost: $0.06. |
| get_coordinated_buysB | Detect coordinated buying across tracked KOLs. Identifies tokens where multiple influencers bought within a short window. Cost: $0.04. |
| check_blacklistC | Check if a token is community-flagged. Community-sourced intelligence with reputation-weighted flags. Cost: $0.02. |
| check_scammer_walletA | Check if a wallet belongs to a known scammer, serial rugger, or flagged entity. Cost: $0.02. |
| get_market_risk_indexA | 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_ruggersC | Known serial rug deployer watchlist. Cross-reference before trusting new tokens. Cost: $0.02. |
| marcus_quickA | 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_forensicsB | 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_ultraA | 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_threadB | X/Twitter-thread-ready forensic analysis. 5-8 posts, each ≤280 chars. Perfect for research posting. Cost: $1.00. |
| watch_tokenA | 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_statusA | 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 | |
TDQS
Scored across 19 tools
The tool set has clear thematic grouping (risk checks, intelligence gathering, AI forensics, monitoring), but there is significant overlap within categories. For example, check_token_risk, check_token_risk_premium, and the marcus_* tools all perform risk analysis with varying depth, which could confuse an agent about which to select for a given scenario. Descriptions help differentiate, but the boundaries are not always sharp.
Most tools follow a consistent verb_noun pattern (e.g., check_batch_risk, get_api_status, watch_token), with clear action prefixes like 'check', 'get', and 'marcus_'. The marcus_* tools deviate slightly by using a proper name prefix instead of a verb, but they maintain internal consistency. Overall, the naming is predictable and readable.
With 19 tools, the count is borderline high for a risk analysis server, leaning toward heavy. While the domain (crypto token security) is complex and may justify many tools, some tools feel redundant (e.g., multiple AI forensic options) or niche (e.g., marcus_thread), suggesting potential over-scoping that could overwhelm an agent.
The tool surface comprehensively covers the domain of crypto token risk assessment and intelligence. It includes core checks (token, deployer, wallet), advanced analysis (AI forensics, holder deep dives), market-wide metrics, social OSINT, and proactive monitoring. There are no obvious gaps; agents can perform end-to-end risk evaluation and monitoring workflows.