Nansen

Tracking token movements

token_flows

Get hourly token-flow history for ONE holder segment over a date range. Use token_recent_flows_summary instead for an on-chain snapshot across ALL wallet categories.

Note: Using holder_segment: smart_money is not a good proxy for an overall market view. Use it only if user explicitly requests it, or to combine it with other non smart money data.

This is a more granular tool than token_recent_flows_summary and provides the TOTAL flows over the entire time frame broken down by segment.

Modes:

onchain_tokens (default): Analyze on-chain tokens by contract address
perps: Analyze Hyperliquid perpetual futures by symbol (chain auto-set to "hyperliquid") — supports native tokens

NOTE: Native tokens (0xeee…, So111…) cannot be queried in onchain_tokens mode. If a native placeholder address is supplied, this tool returns Hyperliquid perpetual-futures flows for that chain's native coin instead (e.g. hyperevm → HYPE, bnb → BNB, base → ETH) and prepends a prominent data-source warning. For native-token wallet-category flows on-chain, use token_recent_flows_summary.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes	TokenFlowsRequest containing parameters and pagination settings

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations provided, the description carries full responsibility for disclosing behavior. It thoroughly explains the native token edge case (returns perps flows with a warning), mode-specific behavior (chain auto-set to hyperliquid in perps mode), and the granularity differences. It also mentions the prominent data-source warning, which is important for interpretability.

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 well-structured with bold section headers and bullet points, making it scannable. It is somewhat longer than necessary due to repeated references to token_recent_flows_summary, but every sentence contributes meaningful information about usage, modes, or edge cases. It earns its length.

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 tool's complexity (multiple modes, parameter interactions, native token handling, and an output schema), the description covers all essential contextual aspects: the type of data, the supported modes, the relationship to sibling tools, and critical caveats. The presence of an output schema removes the need to describe return values, and the description fills in all other knowledge gaps.

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 input schema already provides 100% description coverage for all parameters, so baseline is 3. The description adds value beyond the schema by explaining the practical meaning of modes (onchain_tokens vs perps), the holder_segment smart_money caveat, and how native placeholder addresses are handled. This enriches the schema's bare parameter definitions.

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 opens with a specific verb and resource: 'Get hourly token-flow history for ONE holder segment over a date range.' It clearly distinguishes itself from the sibling token_recent_flows_summary by highlighting granularity and scope. The subsequent modes and examples reinforce the tool's purpose without ambiguity.

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?

The description explicitly names the alternative tool (token_recent_flows_summary) and states when to use it instead: for an on-chain snapshot across ALL wallet categories. It also provides a clear caveat about holder_segment: smart_money not being a good proxy for an overall market view, advising when to use it. This is exemplary guidance.

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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