Nansen

by io.github.hurcanNansen

Checking prediction market trades

prediction_market_trades

Recent trades for a Polymarket market.

When to use:

Source of truth for recent fills and latest trade-tape pricing.
Do not overwrite recent trade prices with older OHLCV candles.

Key fields:

Share Size is quantity; Value USD is dollar value.
Each row is one visible trade leg — Value USD applies to that row, not the whole transaction hash.

Pitfalls:

Large visible trades do not by themselves identify smart money or institutions.

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

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key field semantics (Share Size vs Value USD, per-row value), a pitfall about large trades not indicating smart money, and warns against using OHLCV as a price override. It does not cover pagination behavior or output structure details, but the provided key fields and pitfalls add substantial value beyond the schema.

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 well-organized with clear sections: When to use, Key fields, Pitfalls, and Prerequisites. Each section is concise and adds unique value without redundancy. The opening sentence is direct, and the use of bullets improves scannability.

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?

The description covers prerequisites, key field interpretation, and a common pitfall, which is sufficient for the tool's complexity. An output schema exists, so return values need not be fully explained. However, it misses details about pagination limits and dateRange options, which are relevant for a trades endpoint, preventing a perfect score.

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 is 0% based on the context signal, so the description must compensate. It mentions marketId indirectly via the prerequisite but does not explain the request object structure, dateRange tokens, or page parameter. The 'Key fields' are output fields, not parameters. This leaves parameter semantics largely undocumented.

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 'Recent trades for a Polymarket market' and differentiates from related tools by emphasizing it is the source of truth for trade-tape pricing, distinguishing it from OHLCV and address-level trade tools. The verb 'recent trades' is specific and actionable.

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 'When to use' section explicitly lists use cases and includes an important exclusion (do not overwrite with OHLCV candles). It also provides a prerequisite (call prediction_market_lookup if marketId unknown). However, it does not explicitly contrast with sibling tools like orderbook or address trades, so it falls short of a perfect 5.

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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