prediction_market_address_tradesPrediction market trade history for a Polygon wallet.
Key fields:
Share Size is quantity traded in the displayed outcome side.
Value USD applies to that row only, not the whole transaction.
Pitfalls:
Use this for wallet trade activity, not profitability.
| 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?
No annotations are provided, so the description must carry the burden. It adds useful context about key fields (Share Size, Value USD) and clarifies that value applies per row. However, it does not explicitly state read-only behavior or other operational traits beyond the 'history' implication, so transparency is moderate.
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 compact and well-organized with 'Key fields' and 'Pitfalls' sections. Every sentence adds value, and the structure makes the key facts easy to scan. No redundancy or fluff.
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?
The tool has an output schema, so return format is covered. The description adds important semantics about output fields and clarifies the profitability pitfall. Missing explicit safety/read-only annotation background, but the description is adequate for a query tool with structured schema.
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?
The description does not explain parameters, but the input schema itself contains detailed descriptions for address, page, and dateRange (including token-based date range options). The schema covers the semantics, so the description adds no extra value here. The context signal indicates 0% schema coverage, but the actual schema appears comprehensive, so a baseline score of 3 is appropriate.
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 'Prediction market trade history for a Polygon wallet,' specifying the resource (Polygon wallet), the action (trade history), and scope (address-level). It also distinguishes from sibling tools like prediction_market_address_pnl by noting this is for trade activity, not profitability.
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 description provides an explicit usage note: 'Use this for wallet trade activity, not profitability.' This tells the agent when to use the tool and when to avoid it, though it does not name the alternative sibling tool directly.
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.