Nansen

by io.github.hurcanNansen

Screening prediction markets

prediction_market_screener

Browse and sort Polymarket markets, events, or categories.

When to use:

Broad discovery, screening, and ranked browsing across many markets.
Do NOT use this to resolve one named market/event/slug/URL — use prediction_market_lookup instead.

Query tips:

Literal-style matching on text and slugs, not fuzzy web search.
Prefer one short topic or slug fragment (e.g. fed cuts, zelensky, ncaa tournament).
Do not bundle unrelated topics (e.g. bitcoin ethereum politics weather). If a broad question spans several topics, run separate screener queries for each.
If a query returns no rows, do not invent a nearest match — try a narrower topic or say no data was returned.

Output rules:

Superlatives (highest, leading, biggest, top, trending) must match the shown metric exactly.
Do not infer end dates, rankings, or category leadership from titles alone.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.9/5.0

Behavior5/5

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

No annotations are present, so the description carries the full burden. It discloses literal-style matching, no fuzzy search, no bundling of unrelated topics, no inventing nearest matches, and output rules for superlatives, which are essential behavioral traits.

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?

Front-loaded with a clear purpose, then organized into sections with bold headers. While longer than minimal, every section contributes operational value with no filler.

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?

Covers when to use, query construction, output rules, and error handling. The output schema covers return structure, and the sibling differentiation is clear. Adequate for a complex discovery tool.

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

Parameters4/5

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

The top-level schema has 0% description coverage, but the description adds practical query semantics (prefer one short topic, avoid unrelated bundles) that complement the schema's nested field descriptions. It does not detail mode/status/orderBy, but these are documented in the schema.

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 states 'Browse and sort Polymarket markets, events, or categories' with a specific verb and resources, and explicitly differentiates from prediction_market_lookup for resolving a single named market. This distinguishes it from sibling tools.

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 explicit 'When to use' and 'Do NOT use' guidance, naming the alternative tool for resolving a single market. Query tips further clarify how to structure queries, making the intended use unmistakable.

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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