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CoinRithm

CoinRithm/coinrithm-agent-trading

Official

List open spot orders

list_open_orders
Read-only

Retrieve open spot orders, optionally filtered by coin, with delta polling support to get only changed rows since the last request.

Instructions

List open (resting) spot orders. Omit coinId for ALL open orders across coins, or pass one to filter. Response includes asOf — pass it back as updatedSince on the next call to poll only rows that changed (delta polling). Paper trading only — virtual funds (50,000 mUSD). Not financial advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinIdNoCoin UCID filter. Omit to list ALL open orders.
limitNoMax rows (1-200, default 100).
updatedSinceNoISO 8601 cursor: only orders whose row changed since this instant. Pass the previous response's asOf back here.
agentTraceNoOptional private trace metadata stored in the caller's ledger.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
httpStatusYesHTTP status returned by CoinRithm, or 0 for network errors.
okYesTrue when CoinRithm returned a successful 2xx response.
ledgerEventIdNoPrivate AgentActionEvent id returned by /api/agent/*, when present.
ledgerStatusNoLedger write status header returned by CoinRithm, when present.
bodyNoParsed CoinRithm response body, or raw text when the response is not JSON.
Behavior5/5

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

The description adds significant behavioral context beyond annotations: it specifies paper trading only, virtual funds, and delta polling pattern. No contradiction with annotations (readOnlyHint=true, destructiveHint=false).

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 extremely concise (4 sentences) and front-loaded with the core purpose. Every sentence adds value, with no redundant information.

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 (4 params, nested object, output schema), the description is complete. It covers purpose, usage, parameter details, behavioral context, and delta polling pattern. The output schema covers return format, so no missing information.

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 baseline is 3. The description adds value by explaining the coinId omission for all orders and the asOf callback pattern for updatedSince, enriching the semantic understanding 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 it lists open spot orders, specifies the resource (spot orders) and action (list). It distinguishes from sibling tools like cancel_spot_order and place_spot_order by focusing on listing. The mention of paper trading only adds further purpose context.

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 description provides clear usage guidance on how to filter by coinId or list all orders, and explains delta polling with asOf. However, it does not explicitly mention when not to use this tool or suggest alternatives like get_positions, which prevents a perfect score.

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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