Nansen

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token_info

Get token information — spot on-chain details or Hyperliquid perpetual futures stats.

On-chain tokens mode (default): Returns token details (name, symbol, market cap, FDV, supply, deployment date, socials) and spot trading metrics (volume, buys/sells, buyers/sellers, holders, liquidity).

Perps mode: Returns Hyperliquid perp stats — mark price, funding, open interest, buy/sell pressure, trader participation.

Returns: Token information as markdown.

On-chain tokens fields:
- **Market Cap / FDV**: Market capitalization and fully diluted valuation
- **Circulating / Total Supply**: Token supply metrics
- **Deployed**: When the token was deployed
- **Volume (Total / Buy / Sell)**: Trading volume in USD
- **Buys / Sells**: Number of buy/sell transactions
- **Unique Buyers / Sellers**: Distinct trading addresses
- **Total Holders**: Number of token holders
- **Liquidity**: Available liquidity in USD

Perps fields:
- **Mark Price**: Current perp mark price
- **Price Change**: Change vs previous price
- **Max Leverage**: Maximum leverage offered for the perp on Hyperliquid (e.g. "40x")
- **Funding Rate (hourly/annualized)**: Current funding rate
- **Open Interest**: Total current open interest in USD
- **Volume (Total / Buy / Sell)**: Perp volume in USD
- **Net Flow (Buy - Sell)**: Buy/sell pressure in USD
- **Traders**: Number of traders

Example: On-chain tokens (default mode): { "mode": "onchain_tokens", "chain": "ethereum", "tokenAddress": "0xa0b86a33e6b6c4b3add000b44b3a1234567890ab", "timeframe": "1d" }

Hyperliquid perps:
```
{
  "mode": "perps",
  "tokenAddress": "BTC",
  "timeframe": "7d"
}
```

Notes: - On-chain tokens mode uses contract addresses - Perps mode uses token symbols (e.g. BTC, ETH, HYPE) - Both modes use the same timeframe parameter

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden, and it does well by disclosing return format ('Token information as markdown'), detailing the specific fields returned for each mode, and including example JSON requests. It does not cover error handling or rate limits, but for a read-only info tool, the behavioral disclosure is strong and goes beyond a simple restatement.

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 long but efficiently structured with clear section headers (On-chain tokens mode, Perps mode, Returns, Example, Notes). Every section adds value, and the purpose is front-loaded in the first sentence. The bullet lists for fields and the JSON examples make the content scannable without wasted words.

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

Completeness5/5

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

Given the tool's complexity (two modes, many return fields), the description is remarkably complete: it explains both modes, lists all returned fields, provides usage examples, and includes notes on address formats. The presence of an output schema reduces the need to describe return values, but the description goes beyond that anyway, making it fully self-sufficient.

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?

The schema coverage is 0% per the context signals, but the description compensates with two concrete example JSON requests showing the exact parameter names (mode, chain, tokenAddress, timeframe) and their values. The notes clarify that on-chain mode uses contract addresses while perps mode uses symbols, adding meaningful semantics beyond the bare schema. It doesn't enumerate all chain options or timeframes in the description, but the schema provides those details.

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 it retrieves token information for two distinct modes: on-chain spot details and Hyperliquid perps. It explicitly names the main purpose ('Get token information') and contrasts the two modes, distinguishing it from sibling tools that focus on single data types like token_ohlcv or token_flows. The scope is precise and unambiguous.

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 provides clear usage context by explaining the default mode (on-chain) and the perps mode, along with notes on when to use contract addresses vs symbols. However, it does not explicitly compare this tool to sibling tools or state when to prefer it over alternatives, so it lacks explicit exclusions but gives solid context for use.

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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TDQS

A3.8/5.0

Disambiguation5/5

Each tool has a clearly defined purpose, and overlapping tools (e.g., token_flows vs token_recent_flows_summary, nansen_score_top_tokens vs token_discovery_screener) include explicit guidance on when to use them. Even with similar names like prediction_market_trades and prediction_market_address_trades, the descriptions and parameters make the distinction unambiguous.

Naming Consistency4/5

Most tools follow a domain_prefix_noun pattern (address_, token_, prediction_market_), making them predictable within families. However, outliers like general_search, growth_chain_rank, hyperliquid_leaderboard, and transaction_lookup break the pattern, and some names are long or inconsistently formatted (e.g., smart_traders_and_funds_perp_trades vs smart_traders_and_funds_token_balances).

Tool Count3/5

With 38 tools, the server is far above the typical 3-15 range, making it heavy for agents to navigate. However, Nansen is a broad analytics platform covering wallets, tokens, prediction markets, and smart money activity, so the high count is justifiable as each tool serves a distinct function.

Completeness5/5

The tool set provides comprehensive coverage across token analysis (ohlcv, trading, holders, flows, PnL, technicals), wallet analysis (portfolio, transactions, counterparties), prediction markets (lookup, orderbook, trades, PnL), and discovery. The only obvious omission is NFT support, but it is explicitly documented as out of scope, so no critical dead ends exist.

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