Nansen

Analyzing wallet connections

address_counterparties

Get 25 (per page) addresses or entities with the most common interactions with input addresses Default sort is net value transferred between them. Also returns the top 3 tokens transferred by count for each counterparty

Note: To get related wallets:

Focus on direct value transfers to get most likely addresses.
Include CEX deposit addresses (not withdrawal addresses!) as well.
Also go one level deeper:
- Find addresses that interacted with the most likely addresses.
- Find addresses that deposited to the same CEX deposit (NOT withdrawal!) addresses.
Address structure / string is not important, but the relationship is!

Sorting Options (all fields support "ASC"/"DESC"): Available for sorting: total_volume_usd, volume_in_usd, volume_out_usd, interaction_count

Examples:

Query by single address

{ "address": "0x123...", "sourceInput": "Combined", "groupBy": "wallet", "chain": "ethereum", "timeRange": {"from": "30D_AGO", "to": "NOW"}, "order_by": "total_volume_usd", "order_by_direction": "desc" }

Query by entity

{ "entity_id": "Binance", "sourceInput": "Combined", "groupBy": "entity", "chain": "all", "timeRange": {"from": "7D_AGO", "to": "NOW"} }

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes	Complete request for address counterparties (flattened).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.4/5.0

Behavior4/5

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

No annotations are provided, so the description carries the burden. It transparently discloses pagination (25 per page), default sorting by net value, the return of top 3 tokens, and sorting options. It does not mention limitations like time-range clamping (covered in schema), but adequately describes observable behavior.

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-organized with a core intro, methodology note, sorting options, and examples. It is somewhat lengthy due to the methodology note, but each section serves a purpose and the structure aids comprehension.

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?

With an output schema present and 100% schema description coverage, the description needs only to convey core behavior and usage context. It covers both address and entity query modes, pagination, sorting, and supplementary token data, making it complete for correct invocation.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value by listing sorting fields, noting ASC/DESC support, and providing two complete JSON examples that demonstrate correct parameter combinations. This goes beyond schema descriptions.

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 'Get 25 (per page) addresses or entities with the most common interactions with input addresses' with specific details like default sort and token info. It is specific and distinguishes itself from sibling tools like address_related_addresses by focusing on counterparties and value interactions.

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 'Note: To get related wallets' section provides clear methodology for when to use this tool, including focusing on direct value transfers, CEX deposit addresses, and going one level deeper. While it does not explicitly name alternative tools, it gives strong contextual guidance for the use case.

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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