xportalx

nansen_token_god_mode

Full token analytics with Nansen TGM. Supports natural language like 'analyze VIRTUAL token' or 'who holds the most PEPE?'. Auto-resolves token symbols to addresses. Cost: 0.01 USDC

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "nansen_token_god_mode"
  }
}

Input Schema

Name	Required	Description	Default
`chain`	No	Blockchain (e.g., 'ethereum', 'base', 'solana', 'arbitrum')	ethereum
`input`	No	Natural language query (e.g., 'analyze VIRTUAL token', 'show me PEPE holders')
`query`	No	Natural language query (alternative)
`timeframe`	No	Timeframe for flow data	24h
`tokenAddress`	No	Token contract address OR symbol (e.g., 'VIRTUAL', 'PEPE', '0x...')
`token_address`	No	Token contract address (alternative)

Schema Changelog

Changes observed during successful MCP inspections.

First observedJan 31, 2026

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description discloses useful behavioral traits: it supports natural language, auto-resolves symbols to addresses, and costs 0.01 USDC. However, it does not mention output format, error behavior, rate limits, or any authentication requirements, leaving significant gaps in behavioral transparency.

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 three sentences, front-loaded with the core purpose, and contains no redundant information. Every sentence earns its place: purpose, usage examples, and cost.

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?

The tool has 6 optional params and no output schema, but the description gives enough to select it. Missing return-value details and more explicit guidance on when this tool supersedes the specific Nansen siblings, leaving some context incomplete for an agent to predict behavior fully.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds value by explaining auto-resolution of symbols to addresses and showing how the 'input' parameter works with natural-language queries. This enriches the parameter semantics beyond the schema's straightforward field descriptions.

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 it provides 'Full token analytics' and gives concrete natural-language examples, making its purpose evident. It distinguishes itself from the more specific Nansen sibling tools (holdings, netflows, wallet profiler) by being a general-purpose token analytics interface, though it lacks a precise verb like 'query' or 'analyze'.

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 broad token questions and shows example queries ('analyze VIRTUAL token', 'who holds the most PEPE?'), giving clear context for when to use it. It does not explicitly mention alternatives or exclusions, but the sibling tools are clearly more specialized, so the context is sufficient.

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

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