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Nansen

by io.github.hurcanNansen

Checking prediction market orderbook

prediction_market_orderbook

Live orderbook for a Polymarket market.

When to use:

  • Bid/ask depth, liquidity, and yes-share / no-share order structure.

Key fields:

  • Order Size is share quantity, not USD. Do not describe share size as dollar depth unless you calculate shares × price.

Yes/No price relationship:

  • Yes and No are complementary (Yes + No ≈ $1). A No bid at price $X means willingness to buy No when Yes is near $(1−X).

  • A cluster of No bids at low prices (e.g. $0.20) is resistance for Yes rallying to ~$0.80, NOT a support floor for the current Yes price.

  • When comparing OHLCV odds against orderbook depth, convert No-side prices to Yes-equivalent (1 − No price) before drawing divergence conclusions.

Pitfalls:

  • Do not treat raw no-share prices as bearish yes-share odds — prefer prediction_market_ohlcv for current odds / implied probability.

Prerequisites: If marketId is unknown, call prediction_market_lookup first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.5/5.0
Behavior4/5

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 critical behavioral nuances: order size is share quantity, No/Yes price complementarity, and how to interpret No bids as resistance rather than support. It does not mention pagination or update frequency, but the core interpretation pitfalls are thoroughly covered.

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 organized into clear sections (When to use, Key fields, Yes/No relationship, Pitfalls, Prerequisites). Every section adds critical information with no filler, and the most important caveats are front-loaded after the opening sentence.

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

Completeness5/5

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

Given the complexity of prediction market orderbooks and the presence of an output schema, the description provides rich interpretive context and clear guidance on parameter prerequisites and alternatives. It is complete enough for an agent to correctly select and invoke the tool.

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

Parameters3/5

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

Schema description coverage is 0% for the top-level 'request' parameter, but the nested schema describes marketId and page. The description adds prerequisite context for obtaining marketId via prediction_market_lookup, but does not explain the request wrapper or page parameter, so it only partially compensates.

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 identifies the tool as 'Live orderbook for a Polymarket market', and the title reinforces 'Checking prediction market orderbook'. It distinguishes itself from siblings by specifying bid/ask depth, liquidity, and yes/no order structure, and explicitly contrasts with prediction_market_ohlcv.

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?

Provides an explicit 'When to use' section listing use cases, a 'Pitfalls' section stating when NOT to use it (prefer prediction_market_ohlcv), and a 'Prerequisites' directive to call prediction_market_lookup if marketId is unknown. This is exactly the kind of alternative guidance expected.

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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