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Largest orders that opened positions

get_open_interest_opens
Read-onlyIdempotent

Who opened positions on a HyperCore perp in a window of up to a day (since 2025-03-22 10:49 UTC): the orders that opened longs or shorts, largest value first, with the account and its named wallet, size and value opened, average price, taker or resting, position before and after; plus the window's longs / shorts opened in total and by aggressors. Use it to explain an open interest jump.

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": {
      "coin": "<coin>",
      "from": "<from>",
      "to": "<to>"
    },
    "name": "get_open_interest_opens"
  }
}

Input Schema

NameRequiredDescriptionDefault
toYesEnd (ms, exclusive; aligned up to the second; at most a day after `from`)
coinYesPerp market: BTC, xyz:TSLA
fromYesStart (ms, inclusive; aligned down to the second)
sideNoOnly the orders that opened longs, or shorts
limitNoOrders (default 20)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / from / description
      Previous value: -"Start (ms, inclusive; aligned down to the minute)"New value: +"Start (ms, inclusive; aligned down to the second)"
    • changedInput schema / properties / to / description
      Previous value: -"End (ms, exclusive; aligned up to the minute; at most a day after `from`)"New value: +"End (ms, exclusive; aligned up to the second; at most a day after `from`)"
  2. Added

TDQS

A4.3/5.0
Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations by disclosing the one-day maximum window, the earliest available data (2025-03-22 10:49 UTC), the sort order (largest value first), and the exact shape of what comes back including per-order fields and window aggregates. With no output schema, this return-value disclosure is exactly the burden the description must carry.

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?

Front-loaded with the who/what/when, and the single trailing sentence supplies the use case. The middle clause is a dense run-on enumeration of return fields, but every clause carries information since no output schema exists.

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?

For a five-parameter analytical read with no output schema, it covers scope, temporal availability, sort order, returned fields, and aggregates. An agent has enough to call it correctly and to know what a response will contain.

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 schema already documents each parameter; the description adds meaning by framing the call as a largest-value-first top-N, which clarifies what 'limit' actually truncates, and by restating the one-day ceiling on the from/to window. It stops short of elaborating on the side enum or coin naming beyond the schema's own examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'who opened positions on a HyperCore perp', with an explicit data start (2025-03-22 10:49 UTC) and window cap of one day. It is clearly distinguishable from aggregate siblings like get_open_interest and get_open_interest_cohorts by being order-level and largest-first, though it never names those siblings outright.

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?

Provides a concrete triggering context: 'Use it to explain an open interest jump.' That is real usage guidance rather than none, but there is no explicit when-not or a named alternative (e.g. use get_open_interest for totals only), so the agent must infer the boundary.

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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