prediction_market_address_pnlPrediction market PnL breakdown for a Polygon wallet.
Key fields:
Market-by-market PnL when the API provides it.
Blank PnL fields mean unavailable data, not zero.
Position Size (Shares) is quantity; Position Value USD is current
marked value.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It adds important interpretative details: blank PnL fields mean unavailable data (not zero) and clarifies that position size is quantity while position value is current marked value. This goes beyond a simple statement of purpose and helps avoid misinterpretation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: one main sentence plus a bulleted list of key fields. It is front-loaded with the core purpose and uses structured bullets for clarity. No unnecessary words or repetitions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return format is likely covered there. However, the input schema is complex (a union type under 'request') and the description does not explain how to populate it or that pagination is available via 'page'. The description focuses on output interpretation rather than usage, leaving a notable gap for a tool with nested input options.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% per context, yet the description does not explain the 'request' parameter structure, which is a union type. It only mentions 'Polygon wallet' (mapping to address) but does not clarify the page parameter or how to construct the request. The description provides minimal added value over the schema's sparse parameter info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides a 'Prediction market PnL breakdown for a Polygon wallet.' It specifies the scope (wallet-level) and distinguishes it from sibling tools like prediction_market_address_summary or prediction_market_address_trades. The key fields section further clarifies what is included.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The tool's usage is implied by its name and description (checking address PnL), but there is no explicit guidance on when to choose this over alternatives like prediction_market_pnl_leaderboard or wallet_pnl_for_token. No exclusions or alternative mentions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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).
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.
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.