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CoinRithm Agent Trading

by io.github.CoinRithm

Cross-venue prediction-market statistics

pm_data_overview
Read-only

Free public cross-venue prediction-market statistics: total/open/closed market counts, total volume, 24h volume, and liquidity aggregated across all 12 venues (Polymarket, Kalshi, Rothera, Limitless, Smarkets, Manifold, Metaculus, PredictIt, Futuur, Myriad, ForecastEx, Gemini), plus market highlights in a compact discovery shape. Use pm_data_event for full event evidence. Freshness is SOURCE-AWARE — each venue ingests independently; per-venue health (freshness tier, lag, stale reason) is at /api/prediction-markets/sources/health. Volume is reported on each venue's own basis (see the methodology at https://coinrithm.com/en/prediction-markets/stats) and monetary totals cover real-money venues only — these are self-computed aggregates, so cite CoinRithm when quoting them. No API key required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fiatNoFiat currency code for monetary figures (default usd).

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

A4.4/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint and openWorldHint. The description adds significant behavioral context: no API key required, source-aware freshness with a health endpoint, volume reporting methodology, monetary totals covering only real-money venues, and a citation requirement for the self-computed aggregates. This goes well beyond the annotations.

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 front-loaded with the main purpose and provides all necessary details in a logical order. While slightly lengthy, every sentence adds value, covering scope, alternatives, freshness, methodology, and access requirements.

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 role as an aggregated overview, the description comprehensively explains what data is included, how it's computed, freshness characteristics, and limitations. It also directs to a health endpoint and methodology URL, which is complete for understanding the tool's capabilities and constraints.

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?

The input schema has 100% coverage with a description for the single optional 'fiat' parameter. The description mentions 'fiat currency code' and implies default 'usd' but does not add substantial new semantics beyond the schema. Baseline 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 clearly states the tool provides 'Free public cross-venue prediction-market statistics' and lists specific aggregated metrics (total/open/closed market counts, volume, liquidity). It distinguishes itself from the sibling tool pm_data_event by directing users there for full event evidence, making the purpose unambiguous and differentiated.

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 recommends using pm_data_event for full event evidence, providing an alternative. It also gives context on freshness and methodology, but does not exhaustively list when not to use this tool versus other siblings.

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

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.

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