Nansen

Checking Hyperliquid leaderboard

hyperliquid_leaderboard

Get Hyperliquid perpetual futures trader leaderboard with performance metrics.

Returns: Trader performance rankings as markdown.

Columns returned:
- **Address** / **Label**: trader wallet, and its Nansen label if any
- **Total PnL** (USD): realized + unrealized over the date range
- **Realized PnL** (USD): net, from positions closed in the range. Being a net total it shows no per-trade outcome, so no win rate can be derived from it or any other column here
- **Unrealized PnL** (USD): on positions still open, at the current mark price
- **ROI** (%): Total PnL / (traded notional + open notional) — PnL per dollar traded, not return on capital
- **Volume** (USD): traded notional; both fills of a position count
- **Trades**: number of fills
- **Account Value** (USD): only available for the top 500K traders

This is an overview of top traders and their headline stats. For a trader's open positions, call address_portfolio with mode='hyperliquid'.

Sorting: total_pnl, realized_pnl_usd, unrealized_pnl_usd, roi, volume_usd, total_trades, account_value

Filtering (from/to): totalPnl (USD), accountValue (USD), roi (a fractional ratio, not the percent shown — pass 0.1 for "10% or better", not 10)

Example: { "date": {"from": "7D_AGO", "to": "NOW"}, "accountValue": {"from": 100000, "to": 1000000}, "totalPnl": {"from": 10000}, "order_by": "total_pnl", "orderByDirection": "DESC" } Rank by traded volume instead: "order_by": "volume_usd"

Notes: - Hyperliquid perpetual futures only - Null/empty means data is not available — do not read it as zero

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.9/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden. It discloses the return format (markdown), explains each metric (e.g., ROI defined as PnL per dollar traded, not return on capital), highlights that account value is only for top 500K traders, and notes null means data unavailable. No contradictions.

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?

Well-organized with clear sections (returns, columns, sorting, filtering, example, notes). Front-loaded with purpose and each section adds value without redundancy. The example is concise and illustrative.

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

Completeness4/5

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

Covers all essential aspects for usage: return columns, semantics, sorting/filtering, and an example. Pagination (page parameter) is not discussed, but it exists in the schema and is a minor gap. Overall complete for a leaderboard tool.

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?

Despite schema coverage at 0%, the description enriches parameter understanding: explains sorting fields, filter semantics, and importantly clarifies that the ROI filter expects a fractional ratio (pass 0.1 for 10%) not the percent shown. The example concretely illustrates parameter usage.

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 retrieves the Hyperliquid perpetual futures trader leaderboard with performance metrics. It lists specific columns and differentiates from address_portfolio for open positions, distinguishing it from sibling tools like prediction_market_pnl_leaderboard.

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

Usage Guidelines5/5

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

Explicitly provides usage context: 'This is an overview of top traders and their headline stats' and directs the agent to address_portfolio with mode='hyperliquid' for open positions. This gives a clear when-to-use and an alternative.

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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