Rug Munch Intelligence

get_kol_shills

Detect influencer shill patterns for crypto tokens by analyzing which KOLs promote them, their buy timing versus promotion timing, and coordinated pump activities to identify potential manipulation risks.

Instructions

KOL shill pattern detection: which influencers promote this token, their buy timing vs shill timing, coordinated pump patterns. Cost: $0.06.

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": "get_kol_shills"
  }
}

Input Schema

Name	Required	Description	Default
`token_address`	Yes	Token address

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 the full burden of behavioral disclosure. It mentions cost ($0.06), which is useful context, but lacks details on permissions, rate limits, output format, or error handling. The description implies analysis but doesn't specify behavioral traits like data freshness or processing time.

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 and front-loaded with the core purpose, followed by cost information. Both sentences earn their place by defining functionality and operational context, though it could be slightly more structured for clarity.

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 (analyzing influencer patterns), no annotations, no output schema, and 100% schema coverage, the description is minimally adequate. It covers purpose and cost but lacks details on output, error cases, or integration with sibling tools, leaving gaps for an AI agent.

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?

The input schema has 100% description coverage for the single parameter 'token_address', so the baseline is 3. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints, but doesn't need to compensate for gaps.

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's purpose with specific verbs ('detection', 'promote') and resources ('KOL shill patterns', 'influencers', 'token'), distinguishing it from siblings like 'get_coordinated_buys' or 'get_social_osint' by focusing on influencer promotion timing and coordination analysis.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'get_coordinated_buys' or 'get_social_osint', nor does it mention prerequisites or exclusions. The cost mention hints at usage context but doesn't specify when to choose this over other tools.

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