Nansen

Analyzing token quant scores

token_quant_scores

Get Nansen Score Indicators for a token - quantitative risk and reward signals.

Use this tool when assessing a token's risk/reward profile, evaluating buy/sell decisions, or when the user needs quantitative data to make trading decisions.

Returns: Token risk/reward indicators as markdown with interpretation guidance.

Token info:
- **Market Cap**: Current market cap in USD
- **Market Cap Group**: largecap (>$1B), midcap ($100M-$1B), or lowcap (<$100M)
- **Is Stablecoin**: Whether token is a stablecoin (some indicators don't apply to stablecoins)

Fields returned per indicator:
- **Score**: Signal classification (bullish/neutral/bearish for reward; low/medium/high for risk)
- **Signal**: Raw numeric value of the indicator
- **Percentile**: Rank vs same market cap group (0-100%)
- **Last Trigger**: Date when signal was last calculated

Indicator types:
- **Reward Indicators**: price-momentum, funding-rate, chain-fees, chain-tvl, protocol-fees, trading-range
- **Risk Indicators**: btc-reflexivity, liquidity-risk, token-supply-inflation, concentration-risk, cex-flows

Notes: - Not all indicators available for every token/chain combination - Percentile compares against same market cap group (largecap >$1B, midcap $100M-$1B, lowcap <$100M)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description carries the burden. It discloses important behavioral traits: not all indicators are available for every token/chain, percentile comparisons are per market cap group, and stablecoins have limitations. It also implies this is a read-only operation by using 'Get' and describing returns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with sections (summary, usage, returns, notes). It is front-loaded with the main purpose and uses clear formatting. Each section adds value, though the detailed return explanation is partially redundant given the existence of an output schema.

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

Completeness3/5

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

The description thoroughly covers output semantics, usage context, and limitations, but entirely omits input parameter explanations. Given the tool's complexity (nested request with anyOf) and the presence of an output schema, the description should at least mention what parameters are needed, making it incomplete.

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%, so the description must compensate for parameter guidance. It does not mention the 'request' parameter, tokenAddress, or chain at all. The schema provides some descriptions, but the tool description adds no input semantics, leaving the complex nested request object unexplained.

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 the tool's purpose: 'Get Nansen Score Indicators for a token - quantitative risk and reward signals.' This uses a specific verb and resource, and distinguishes it from sibling tools like token_technical_indicators and nansen_score_top_tokens by focusing on risk/reward quant scores.

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?

It explicitly states when to use: 'Use this tool when assessing a token's risk/reward profile, evaluating buy/sell decisions, or when the user needs quantitative data to make trading decisions.' This provides clear context, though it does not name exclusions or specific alternatives.

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.

Resources

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