Nansen

by io.github.hurcanNansen

Checking prediction market PnL leaders

prediction_market_pnl_leaderboard

PnL leaderboard for a Polymarket market.

When to use:

Only for profitability claims.

Key fields:

Side Held reflects current side exposure where the API provides it.

Pitfalls:

If PnL fields are blank, say profitability is unavailable — do not substitute top holders or position size.

Prerequisites: If marketId is unknown, call prediction_market_lookup first.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description adds valuable behavioral context: the 'Side Held' field reflects current side exposure where available, and if PnL fields are blank, the agent should say profitability is unavailable rather than substituting top holders or position size. It doesn't cover all behaviors (e.g., pagination, data freshness) but provides meaningful guidance beyond a simple operation description.

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 compact and well-structured with bold headers for 'When to use', 'Key fields', 'Pitfalls', and 'Prerequisites'. It front-loads the core statement and uses bullets for clarity, with no unnecessary words.

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 description covers purpose, usage scope, pitfalls, and prerequisites, and the output schema handles return values. However, the request parameter is underspecified, and with no annotations, the description is the only source of behavioral transparency. This leaves a notable gap for a tool with a nested input object.

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

Parameters2/5

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

Schema description coverage for the top-level 'request' parameter is 0%, and the description only mentions marketId in the prerequisite. It does not explain the request object structure, the page parameter, or how to format the request. The nested schema has descriptions, but the description itself fails to compensate for the low coverage.

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 'PnL leaderboard for a Polymarket market' and the title says 'Checking prediction market PnL leaders'. It identifies a specific resource (Polymarket market) and operation (viewing PnL leaderboard), distinguishing it from siblings like prediction_market_top_holders and token_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 Guidelines4/5

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

The description includes an explicit 'When to use' section stating 'Only for profitability claims' and a prerequisite to call prediction_market_lookup if marketId is unknown. This provides clear context, but it doesn't explicitly name alternative tools or state when not to use beyond the profitability scope.

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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