Rug Munch MCP Server

rug-munch-mcp PyPI v1.0.1

Published by cryptorugmunch — no publish provenance, so origin is unverified, but the source is public: the repository link below is self-declared yet readable, so you can inspect the code before adopting it.

MCP server for Rug Munch Intelligence — 19 tools for crypto token risk analysis, rug pull detection, and AI forensics

Trust grade
A
93/100
Last scanned get badge →
Trust
A · 93/100
Adoption risk for you: the threat score, then adjusted down for blast radius, publisher verification and how much the scan could see. Deterministic; every point is auditable.
Capability
High
Blast radius if it went rogue — what the server’s tools could reach. Independent of trust.
Coverage
Source
How much the scan could actually inspect. Shallow coverage is stated, never hidden.
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A Why this grade threat 100 − adoption risk = 93/100

The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.

1. Threat score — 100 − 0 = 100. What the published surface and source actually contain:

The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.

2. Client adoption risk — 100 − 7 = 93. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:

PointsAdoption-risk factor
−6 capability blast radius (high) — client exposure if the model is manipulated
−1 publisher verification (public source) — no provenance, but the source is public and inspectable

Capability observations and info notes are shown under Findings but never scored. Open any row's finding below for the file, line and evidence behind a deduction.

Findings 2

high Sensitive-source and external-sink co-existMTC-FLOW-004

Tools that read sensitive data ([get_token_intelligence]) and tools that can send data out ([watch_token]) are exposed together. An agent can move private data to the sink.

Evidence: sources [get_token_intelligence] → sinks [watch_token]

Fix: Keep secret-reading and egress capabilities on separate, separately-approved servers.

Location: flow get_token_intelligence → watch_token

medium Unconstrained URL/host parameter "webhook_url" on "watch_token"MTC-CAP-007

Tool "watch_token" takes a URL/host parameter "webhook_url" with no allowlist/pattern. An outbound-request tool with an unbounded destination enables SSRF and cloud-metadata access (e.g. 169.254.169.254).

Fix: Allowlist destinations or constrain the parameter; block private/link-local addresses server-side.

Location: tool watch_token · inputSchema.properties.webhook_url

Tools 19

Each tool and what it can reach — statically extracted from the published source.

  • get_token_intelligencereads sensitive data
  • watch_tokennetwork egress
  • check_batch_riskno sensitive capability
  • check_blacklistno sensitive capability
  • check_deployer_historyno sensitive capability
  • check_scammer_walletno sensitive capability
  • check_token_riskno sensitive capability
  • check_token_risk_premiumno sensitive capability
  • get_api_statusno sensitive capability
  • get_coordinated_buysno sensitive capability
Show 9 more tools ↓
  • get_holder_deepdiveno sensitive capability
  • get_kol_shillsno sensitive capability
  • get_market_risk_indexno sensitive capability
  • get_serial_ruggersno sensitive capability
  • get_social_osintno sensitive capability
  • marcus_forensicsno sensitive capability
  • marcus_quickno sensitive capability
  • marcus_threadno sensitive capability
  • marcus_ultrano sensitive capability

Toxic flows 1

Cross-tool combinations that form a data-exfiltration primitive (untrusted input → sensitive source → external sink).

What this scan could not see

Versions 1

Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.

VersionScoreFindingsEngineScanned
v1.0.1 latest A 93/100 2 1.13.0 2026-08-25

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Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.

MCP Trust Score: A · 93/100
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Verify this score yourself

The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.

npx mcptrustchecker scan rug-munch-mcp --online --registry pypi

Use the free API → How scoring works

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