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Nansen

by io.github.hurcanNansen

Discovering trending tokens

token_discovery_screener

Get comprehensive token screening data across multiple blockchain networks with advanced filtering.

A maximum of 25 results per page are returned out of 1000s of tokens. Use the sorting and filtering options to narrow down the results. In a mixed spot + hyperliquid request, page applies to both the spot and perps sections. A maximum of 5 chains can be specified per request (excess chains are automatically trimmed).

This tool helps with token discovery and finding trending tokens by combining different metrics: volume, liquidity, market cap, smart money activity, and token age.

IMPORTANT - Hyperliquid Special Case:

  • Hyperliquid chain queries perpetual futures (perps), not spot tokens

  • When hyperliquid is mixed with other chains, two sections of up to 25 results each are returned - one for spot tokens and one for perps.

  • For perps, only these filters are supported: volume, buyVolume, sellVolume, openInterest, netflow, nofTraders, traderType

  • Additional orderBy fields for perps: open_interest, funding (e.g. orderBy 'funding' ascending finds perps that pay longs)

  • Unsupported filters/orderBy will fallback to defaults

INPUT EXAMPLES:

Find tokens which are going up in price.

Added some liquidity filter to remove spam and low quality tokens.

{
    "chains": ["ethereum", "solana", "bnb", "base"],
    "timeframe": "24h",
    "liquidity": {"from": 100000},
    "nofTraders": {"from": 10},
    "orderBy": "price_change",
    "orderByDirection": "desc"
}

Find top stablecoins by market cap

{
    "chains": ["ethereum", "solana", "bnb", "base"],
    "timeframe": "7d",
    "sectors": ["Stablecoin"],
    "orderBy": "market_cap_usd",
    "orderByDirection": "desc"
}

Find AI memecoins with high trading activity

{ "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "7d", "sectors": ["AI Meme"], "liquidity": {"from": 100000}, "volume": {"from": 1000000} }

Find DeFi lending tokens

{ "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "24h", "sectors": ["DeFi Lending (Money Markets)"], "netflow": {"from": 1000000} }

Find tokens which have a lot of buying activity (high nofBuyers and buyVolume)

Note that we added some filters to remove spam and low quality tokens. We added liquidity filter so that we only surface tokens which we can buy or sell.

We sort by netflow descending to get tokens with the most net buying activity.

{
    "chains": ["ethereum", "solana", "bnb", "base"],
    "timeframe": "24h",
    "liquidity": {"from": 100000},
    "buyVolume": {"from": 1000000},
    "marketCapUsd": {"from": 1000000},
    "nofBuyers": {"from": 10},
    "orderBy": "netflow",
    "orderByDirection": "desc"
}

Find Hyperliquid perps with high open interest and positive net flow

{
    "chains": ["hyperliquid"],
    "timeframe": "7d",
    "openInterest": {"from": 100000},
    "volume": {"from": 1000000},
    "netflow": {"from": 0},
    "nofTraders": {"from": 10},
    "orderBy": "netflow",
    "orderByDirection": "desc"
}

WARNING: To avoid timeouts, it's recommended to:

  • Use 4 chains or less at a time (API tends to timeout with more chains)

  • Use shorter timeframes (e.g., 24h or 1h instead of 7d or 30d)

Args:

Returns: Comprehensive token metrics as markdown. Returns empty string if no tokens found.

Columns returned:
- **Token Address**: Token address (e.g., 0x1234567890123456789012345678901234567890)
- **Symbol**: Token trading symbol (e.g., ETH, BTC, DOGE)
- **Chain**: Blockchain network (ethereum, solana, polygon, etc.)
- **Price USD**: Current token price in USD (currency formatted)
- **Price Change**: Price change percentage over the date range (percentage, can be negative)
- **Market Cap**: Current market capitalization (currency formatted)
- **Fully Diluted Valuation (FDV)**: Market cap if all tokens were circulating (currency formatted)
- **FDV/MC Ratio**: Ratio indicating how much supply is locked/vested (numeric, >1 means locked supply)
- **USD Volume**: Total trading volume in USD (currency formatted)
- **Buy USD Volume**: Total buy volume in USD (currency formatted)
- **Sell USD Volume**: Total sell volume in USD (currency formatted)
- **Net Flow USD**: Net flow (buys minus sells) in USD (currency formatted, can be negative)
- **DEX Liquidity**: Available liquidity for trading (currency formatted)
- **Inflow/FDV**: Inflow as percentage of FDV (percentage formatted)
- **Outflow/FDV**: Outflow as percentage of FDV (percentage formatted)
- **Token Age (Days)**: Days since token was first deployed
- **Sectors**: List of token sectors/categories

Hyperliquid perps columns (smart-money mode, when `onlySmartTradersAndFunds=true`):
- **Net Position** (`LONG $X` / `SHORT $X` / `FLAT`): current net direction. Use this when answering long/short questions.
- **Current Longs USD** / **Current Shorts USD**: gross notional on each side; sizing only, not direction.
- **Net Position Change**: delta over the timeframe — can be positive while Net Position is still SHORT.

Notes: - Positive Net Flow on spot tokens indicates more buying than selling - High FDV/MC Ratio suggests significant locked or vested tokens

Filtering Options (filters parameter): - Numeric Ranges: volume, liquidity, marketCapUsd, netflow, tokenAgeDays, nofTraders, nofBuyers, nofSellers, nofBuys, nofSells, buyVolume, sellVolume, fdv, fdvMcRatio, inflowFdvRatio, outflowFdvRatio - Categories: sectors (e.g. ["AI", "Meme"]), includeSmartMoneyLabels - Asset class toggles (spot chains only, default false for both): includeStablecoins, includeNativeTokens. Set true only when the user asks for stablecoins/native tokens specifically. - Trader Type: traderType (string: "all", "sm", "whale", "public_figure") - Use "sm" ONLY when user explicitly asks for "smart money". - Use "whale" ONLY when user specifically asks for whales or large holders. - Use "public_figure" ONLY when user asks for KOLs or popular figures. - Data with "sm", "whale", and "public_figure" is sparse — "whale" and "public_figure" are even sparser than "sm". Pairing any of these with other filters (volume, liquidity, netflow) is likely to return no results. - Only pair traderType="sm/whale/public_figure" with other filters (volume, liquidity, netflow) if the user request explicitly requires it. - Instead of pairing this with other filters, you can rely on orderBy to sort by netflow, volume, liquidity, etc.

**CRITICAL WARNING:** 'priceChange' is NOT a valid filter. You cannot filter for "tokens up > 10%". Use `orderBy="priceChange"` instead.

Sorting Options (orderBy field): Available fields (use with orderByDirection: "asc" or "desc"):

- **priceUsd**: Sort by token price
- **priceChange**: Sort by price change percentage
- **marketCapUsd**: Sort by market capitalization
- **volume**: Sort by total trading volume
- **buyVolume**: Sort by buy volume
- **sellVolume**: Sort by sell volume
- **netflow**: Sort by net flow (buys - sells)
- **liquidity**: Sort by DEX liquidity
- **nofTraders**: Sort by number of traders

(Note: Fields like `tokenAgeDays` or `outflowFdvRatio` are for FILTERING only, not sorting)

Default: orderBy="netflow", orderByDirection="desc"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses the 25-result page limit, 5-chain cap with auto-trimming, mixed spot+perps pagination behavior, fallback of unsupported filters/orderBy to defaults, timeout-avoidance recommendations, and the fact that an empty string is returned when no tokens are found.

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 lengthy but well-organized with headers, examples, warnings, returns, filtering, and sorting sections, and it front-loads the core purpose and key constraints. It is slightly overlong and contains some repeated Hyperliquid notes that could be tightened, but every major section earns its place given the tool's complexity.

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 no annotations and a sparse top-level schema, the description is exhaustive. It covers pagination, chain limits, Hyperliquid perps vs spot behavior, return columns, semantic notes, filtering and sorting options, timeout warnings, and the critical priceChange caveat. Nothing essential for correct invocation is missing.

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

Parameters5/5

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

The top-level request parameter has no schema description (0% coverage), so the description must compensate and does extensively. It explains the invalid 'priceChange' filter, traderType usage rules and data sparsity, includeStablecoins/includeNativeTokens defaults, perps-only filters like openInterest, and the default orderBy, all well beyond what the schema states.

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 opens with 'Get comprehensive token screening data across multiple blockchain networks with advanced filtering' and explicitly states it serves token discovery and trending-token searches. This is a specific verb+resource statement, but it does not explicitly differentiate it from sibling screening tools such as prediction_market_screener.

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 gives strong context for when to use the tool: 'This tool helps with token discovery and finding trending tokens' and includes six worked examples covering price gainers, stablecoins, AI memecoins, DeFi lending, buy activity, and Hyperliquid perps. It does not name alternative tools or provide exclusion criteria, so it is clear context without explicit when-not-to-use guidance.

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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