Skip to main content
Glama

CoinRithm Agent Trading

by io.github.CoinRithm

Get my trades

get_my_trades
Read-only

Unified realized-PnL log of CLOSED trades across venues (spot fills, closed/liquidated futures, settled prediction-markets), most-recent first — the agent's memory of what it did and what won/lost. Use it to review performance before deciding the next move. Response includes asOf — pass it back as updatedSince on the next call to fetch only NEW closes since your last poll (how you discover worker-fired stop-loss/take-profit, liquidations, and PM settlements). Paper trading only — virtual funds (50,000 mUSD). Not financial advice. Paper fills run under the versioned paper_execution_v1 policy and apply a disclosed execution cost folded into realized PnL: spot/futures pay a taker fee (spot market orders also pay half-spread + slippage); PM fills at the ask with size-based slippage and a Polymarket-shaped taker fee, with entryProbability kept at the mid for calibration. See the executionModel in quote/trade results — a rehearsal cost, not an exchange fill guarantee.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows (1-100, default 25).
venueNoFilter by venue (default all).
agentTraceNoOptional private trace metadata stored in the caller's ledger.
updatedSinceNoISO 8601 cursor: only trades closed/settled since this instant. Pass the previous response's asOf back here.

Output Schema

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

TDQS

A4.6/5.0
Behavior5/5

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

The description extensively details behavioral traits beyond annotations: paper trading with virtual funds, execution cost modeling, fee structures, and the polling mechanism via 'asOf'. Annotations (readOnlyHint, openWorldHint, destructiveHint) are consistent and supplemented with rich context.

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, front-loading the core purpose and usage. While verbose, each sentence provides essential context for an AI agent (paper trading details, execution model). Minor redundancy in execution fee breakdown.

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, output schema, many siblings), the description covers all necessary aspects: purpose, usage, behavior, parameter semantics, and special considerations (paper trading, polling). It is self-contained and complementary to the schema and annotations.

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 covers all 4 parameters fully. The description adds value by explaining the cursor pattern for 'updatedSince' and the context of 'agentTrace' (trace metadata). This goes beyond the 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 defines the tool as a unified realized-PnL log of closed trades across venues, acting as the agent's memory. It distinguishes from siblings like 'get_positions' (open positions) and 'get_performance' (aggregated metrics).

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 explicitly states to use it for reviewing performance before deciding the next move and explains the polling pattern with 'updatedSince'. While it doesn't list explicit when-not-to-use scenarios, the context is clear and covers the primary use case.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All CoinRithm Agent Trading toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct resource or action (e.g., spot vs futures vs PM, quote vs open vs close, different PM data endpoints). Descriptions provide clear context and usage guidance, eliminating ambiguity.

Naming Consistency5/5

All tools follow a verb_noun pattern in snake_case (e.g., `cancel_spot_order`, `open_futures_position`, `pm_data_event`). Even `whoami` is a common exception. Naming is uniform and predictable.

Tool Count4/5

35 tools is on the high side but appropriate for a multi-venue trading platform covering spot, futures, prediction markets, analytics, and account management. Each tool serves a distinct purpose, though minor consolidation could be possible.

Completeness5/5

The tool surface covers the full trading lifecycle: quotes, order placement, cancellation, position management, SL/TP, portfolio tracking, performance, and extensive market data. No obvious gaps for the stated purpose.

Resources