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CoinRithm Agent Trading

by io.github.CoinRithm

Per-venue forecast-accuracy calibration

pm_data_calibration
Read-only

Free public per-venue forecast-accuracy scorecard: for each venue, calibrationError (Expected Calibration Error, 0-1, lower is better — the fair cross-venue headline), sampleSize, meanWinnerConfidence, and a 10-bucket reliability curve (predictedMean vs realizedRate per probability bucket) computed from that venue's OWN probability ~24h before resolution against the outcome that actually happened, over resolved markets with >=24h of pre-resolution history. Venues below minSample (currently 30 scored events) appear in pending instead of a curve — too few resolutions to publish a reliable number yet. Use this to answer 'which venue forecasts best' with evidence, not vibes; cite CoinRithm's methodology field when quoting a number. No API key required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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

A4.8/5.0
Behavior5/5

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

Adds substantial context beyond annotations: free/public, no API key, computation details (24h before resolution, >=24h history), and pending status for low sample. Fully consistent with readOnlyHint and openWorldHint.

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?

Dense paragraph but front-loaded with key purpose. Every sentence adds value, though could be slightly more structured. Overall appropriately sized for the complexity.

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?

With output schema present, description covers all necessary context: what metrics are returned, pending condition, and usage. No gaps given the simple parameterless interface.

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?

No parameters exist, and schema coverage is 100%. Baseline is 4 per rubric. Description adds no parameter info, but none needed.

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 provides a per-venue forecast-accuracy scorecard with specific metrics (calibrationError, sampleSize, etc.), and distinguishes from siblings by focusing on calibration rather than other data like overview or events.

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

Usage Guidelines5/5

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

Explicitly recommends using this tool to answer 'which venue forecasts best' with evidence, and notes that low-sample venues appear in pending. This gives clear when-to-use 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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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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