Rug Munch Intelligence

marcus_ultra

Analyze crypto tokens for forensic risk detection using AI-powered pattern recognition to identify potential rug pulls and scams before transactions.

Instructions

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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "token_address": "<token_address>"
    },
    "name": "marcus_ultra"
  }
}

Input Schema

Name	Required	Description	Default
`token_address`	Yes	Token mint address (Solana) or contract address (EVM)
`chain`	No	Blockchain: solana, ethereum, base, arbitrum, polygon, optimism, avalanche	solana

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.1Mar 13, 2026

TDQS

A4.1/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and discloses key behavioral traits: it's a paid service ('Cost: $2.00'), has extended processing time ('~30-120s'), and uses advanced AI ('Claude Opus 4 with extended reasoning'). It does not cover aspects like error handling or output format, but provides substantial operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise and front-loaded, with every sentence adding value: it defines the tool's purpose, capabilities, processing time, and cost in a compact format without unnecessary words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (AI forensic analysis) and no output schema, the description is moderately complete. It covers purpose, capabilities, time, and cost, but lacks details on output format, error cases, or limitations. With no annotations, it should provide more behavioral context for a high-stakes analysis tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters fully. The description does not add any meaning beyond the schema, such as explaining how the token_address is used in analysis or chain implications. Baseline 3 is appropriate as the schema handles parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs 'deepest AI forensic analysis' with specific capabilities like 'connected wallet clusters, timing correlations, historical rug playbook matching.' It distinguishes from siblings by emphasizing depth and extended reasoning, unlike quick checks or basic risk assessments.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for in-depth forensic analysis on tokens, with context from sibling tools suggesting alternatives like 'marcus_quick' for faster checks or 'check_token_risk' for basic assessments. However, it does not explicitly state when to use this tool versus those alternatives, missing direct comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.