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by EmblemCompany
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nansen: nansen_who_bought_sold

nansen_who_bought_sold
Read-onlyIdempotent

Identify smart money wallets that bought or sold a specific token within a date range. Filter by chain, token address, and trader label to track wallet activity.

Instructions

See which smart money wallets bought or sold a specific token in a date range.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "chain": "<chain>",
      "token_address": "<token_address>",
      "date_from": "<date_from>",
      "date_to": "<date_to>"
    },
    "name": "nansen_who_bought_sold"
  }
}

Input Schema

NameRequiredDescriptionDefault
chainYesChain the token is on
token_addressYesToken contract address
date_fromYesStart date (YYYY-MM-DD)
date_toYesEnd date (YYYY-MM-DD)
label_typeNoTrader label filter (smart_money, all_traders)smart_money
per_pageNoResults per page

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added
  2. Removedv1.1.0
  3. First observedv1.0.0

TDQS

A3.6/5.0
Behavior2/5

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

Annotations already cover read-only, open-world, and non-destructive behavior, lowering the burden on the description. However, the description adds no additional behavioral details beyond restating the query scope; it omits return format, pagination behavior, or any caveats about the open-world nature.

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 a single, concise sentence that front-loads the action and expected outcome. Every word contributes meaning, with no filler or redundancy.

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

Completeness4/5

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

For a straightforward read-only list query with complete parameter descriptions, the description plus schema is sufficient for an agent to invoke the tool. It does not explicitly describe the output shape, but no output schema exists; the expected result (a list of wallets) is implicit and adequately conveyed.

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 coverage is 100%, with descriptions for all six parameters, including enum values for chain and label_type. The description adds no extra parameter detail beyond what the schema provides, so the baseline score of 3 is appropriate.

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 uses a specific verb ('See') and clearly identifies the resource ('which smart money wallets bought or sold a specific token') and scope ('date range'). It distinguishes itself from sibling tools like nansen_smart_money_trades by focusing on wallet identities rather than the trade stream.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the use case—finding smart money buyers/sellers for a token over a date range—but does not explicitly compare with alternatives such as nansen_smart_money_trades or mention when not to use it. It provides clear context but lacks exclusion or alternative 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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