Rug Munch Intelligence

marcus_forensics

Analyze crypto token risks with AI forensic investigation covering deployer history, holder patterns, social OSINT, contract security, liquidity, and trading patterns to detect potential scams before transacting.

Instructions

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.

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_forensics"
  }
}

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

B3.4/5.0

Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses time (~15-60s) and cost ($0.50) traits, which are valuable behavioral details. However, it doesn't mention error conditions, rate limits, authentication needs, or what specific output format to expect, leaving gaps for a comprehensive investigation tool.

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

Conciseness4/5

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

The description is appropriately sized with two sentences: the first states purpose and scope, the second provides time and cost. It's front-loaded with the core functionality. Minor improvement could come from slightly better structuring of the investigation areas list.

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?

For a comprehensive investigation tool with 2 parameters, no annotations, and no output schema, the description provides adequate purpose and behavioral context (time/cost). However, it lacks details on output format, error handling, and differentiation from sibling tools, making it minimally complete but with clear gaps.

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 thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., no examples of valid addresses or chain selection guidance). Baseline 3 is appropriate when schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the tool performs a 'full AI forensic investigation' covering multiple specific areas (deployer history, holder patterns, social OSINT, etc.), which provides a comprehensive verb+resource statement. However, it doesn't explicitly differentiate from sibling tools like 'marcus_quick' or 'marcus_ultra' that likely offer similar but different-scoped investigations.

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

Usage Guidelines3/5

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

The description implies usage for comprehensive token investigation with a time estimate (~15-60s) and cost ($0.50), providing some context. However, it doesn't explicitly state when to use this tool versus alternatives like 'marcus_quick' or 'check_token_risk', nor does it mention any exclusions or prerequisites for use.

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